{"cells":[{"cell_type":"markdown","id":"worthy-texture","metadata":{"jupyter":{},"tags":[],"slideshow":{"slide_type":"slide"},"id":"17E9358BAF0944F9BE483A6AA48CF0B3","trusted":true,"mdEditEnable":false},"source":"## 数据集成"},{"cell_type":"markdown","id":"virgin-chosen","metadata":{"jupyter":{},"tags":[],"slideshow":{"slide_type":"slide"},"id":"6B5B449B378A470E81290A7990347520","trusted":true,"mdEditEnable":false},"source":"在这里，中国社会宏观经济指标数据量比较多，在这里我们需要的对数据进行暴力集成，当然是通过他们的共同属性年月  \n当然在数据集成的同时我们需要对数据进行简单了解"},{"cell_type":"code","execution_count":1,"id":"hazardous-premium","metadata":{"jupyter":{},"tags":[],"slideshow":{"slide_type":"slide"},"id":"DB7D6DF232A44356A57774D85DF778CC","trusted":true,"collapsed":false,"scrolled":false},"outputs":[],"source":"# 导入需要的库\nimport pandas as pd\nimport numpy as np\nimport matplotlib.pyplot as plt\nimport warnings\nimport datetime\nimport seaborn as sns\nsns.set_style(\"darkgrid\")\n# 为防止一些警告产生，我们将它给忽略\nwarnings.filterwarnings(\"ignore\")\n%matplotlib inline"},{"cell_type":"markdown","id":"lesbian-ultimate","metadata":{"jupyter":{},"tags":[],"slideshow":{"slide_type":"slide"},"id":"C73779BC4E294A0D94F147C49BD81DFF","trusted":true,"mdEditEnable":false},"source":"### 导入数据"},{"cell_type":"code","execution_count":6,"id":"ordered-adrian","metadata":{"jupyter":{},"tags":[],"slideshow":{"slide_type":"slide"},"id":"82CD83412E6248E1BCD17B78194D6340","trusted":true,"collapsed":false,"scrolled":false},"outputs":[],"source":"# 数据表有点多，虽然方法有一点不够聪明，但是效果真的是很好\nPPI=pd.read_csv(\"../input/gdpp14698/PPI.csv\")\nPMI = pd.read_csv(\"../input/gdpp14698/PMI.csv\")\nFDI = pd.read_csv(\"../input/gdpp14698/FDI.csv\")\nCPI = pd.read_csv(\"../input/gdpp14698/CPI.csv\")\nNation_tax=pd.read_csv(\"../input/gdpp14698/National_Tax_Revenue.csv\")\nRMB_Foreg = pd.read_csv(\"../input/gdpp14698/RMB-Foreign_Currency_Deposit.csv\")\nFiscal_Re=pd.read_csv(\"../input/gdpp14698/Fiscal_Revenue.csv\")\nUrban_Fixed=pd.read_csv(\"../input/gdpp14698/Urban_Fixed_Assets_Investment.csv\")\nDeposit=pd.read_csv(\"../input/gdpp14698/Deposit-Reserve_Ratio.csv\")\nHousing_price = pd.read_csv(\"../input/gdpp14698/Housing_Price_Index.csv\")\nIndustrial_add=pd.read_csv(\"../input/gdpp14698/Industrial_Added_Value.csv\")\nImport_export=pd.read_csv(\"../input/gdpp14698/Import_Export.csv\")\nMoney_supply=pd.read_csv(\"../input/gdpp14698/Money_Supply.csv\")\nBank_Transfer=pd.read_csv(\"../input/gdpp14698/Bank_Transfer.csv\")\nEnterprise_Confidence=pd.read_csv(\"../input/gdpp14698/Enterprise_Confidence.csv\")\nCPGI=pd.read_csv(\"../input/gdpp14698/CGPI.csv\")\nRSCG=pd.read_csv(\"../input/gdpp14698/RSCG.csv\")\nCCI=pd.read_csv(\"../input/gdpp14698/CCI.csv\")\nGold_foregin=pd.read_csv(\"../input/gdpp14698/Gold_Foregin_Exchange_Reserves.csv\")\nForeign_Exchange=pd.read_csv(\"../input/gdpp21332/Foreign_Exchange_Loan.csv\")\nNew_Credit=pd.read_csv(\"../input/gdpp21332/New_Credit.csv\")\nCHIBOR=pd.read_csv(\"../input/gdpp21332/CHIBOR.csv\")\nRate_Adjustment=pd.read_csv(\"../input/gdpp21332/Rate_Adjustment.csv\")\nGas_price=pd.read_csv(\"../input/gdpp21332/Gas_Price.csv\")\nNational_Sock_Trading=pd.read_csv(\"../input/gdpp21332/National_Stock_Trading.csv\")\nStock_Accountt_Status = pd.read_csv(\"../input/gdpp21332/Stock_Accountt_Status.csv\")"},{"cell_type":"markdown","id":"positive-barrel","metadata":{"jupyter":{},"tags":[],"slideshow":{"slide_type":"slide"},"id":"470134DE590C428E8A1BCF5EBE439BDB","trusted":true,"mdEditEnable":false},"source":"数据集成之前，进行数据清洗，删除数据表中的“%”，“-”等不规则噪音，将数据该什么样子，转换成什么样子"},{"cell_type":"code","execution_count":7,"id":"basic-check","metadata":{"jupyter":{},"tags":[],"slideshow":{"slide_type":"slide"},"id":"E458FF25F9864DF28239148088524807","trusted":true,"collapsed":false,"scrolled":false},"outputs":[],"source":"def clear_percent(stamp_str):\n    if \"%\" in stamp_str:\n        return float(stamp_str[:-1])/100\n    elif \"-\" in stamp_str:\n        return None"},{"cell_type":"code","execution_count":8,"id":"affiliated-black","metadata":{"jupyter":{},"tags":[],"slideshow":{"slide_type":"slide"},"id":"EF92FDA80BFB4CC197F0576F589FC575","trusted":true,"collapsed":false,"scrolled":false},"outputs":[],"source":"def cleardata(stampdata):\n    for col in stampdata.columns:\n        if \"YOY\" in col:\n            stampdata[col] = stampdata[col].apply(clear_percent)\n        elif \"Rate\" in col:\n            stampdata[col] = stampdata[col].apply(clear_percent)\n        elif col==\"Month\":\n            stampdata[\"Month\"]=pd.to_datetime(stampdata[\"Month\"],format=\"%Y年%m月份\")"},{"cell_type":"markdown","id":"familiar-exposure","metadata":{"jupyter":{},"tags":[],"slideshow":{"slide_type":"slide"},"id":"0C6108590AA240A7835BE70908629026","trusted":true,"mdEditEnable":false},"source":"### 数据简单清洗与可视化\n在数据集成的同时，我们定义如上面函数，对数据进行简单清洗，以及可视化要求，来帮助我们了解数据  \n下面一般步骤是：  \n  * 清洗数据\n  * 可视化\n  \n遇到及个别数据比较难清洗我们加以单独清洗"},{"cell_type":"code","execution_count":9,"id":"brutal-domestic","metadata":{"jupyter":{},"tags":[],"slideshow":{"slide_type":"slide"},"id":"DEC673AFBB694B8094E8A468BFC92ED8","trusted":true,"collapsed":false,"scrolled":false},"outputs":[],"source":"PPI[\"Curent_Month_YOY\"]=PPI[\"Curent_Month_YOY\"].apply(clear_percent)\nPPI[\"Month\"]=pd.to_datetime(PPI[\"Month\"],format=\"%Y年%m月\")"},{"cell_type":"code","execution_count":10,"id":"efficient-contract","metadata":{"jupyter":{},"tags":[],"slideshow":{"slide_type":"slide"},"id":"48051CAAB8444A058C3BA0DC6764AFA2","trusted":true,"collapsed":false,"scrolled":false},"outputs":[],"source":"PPI.columns=[\"Month\",\"PPI\",\"PPI_Curent_Month_YOY\",\"PPI_Total\"]"},{"cell_type":"code","execution_count":11,"id":"considered-happening","metadata":{"jupyter":{},"tags":[],"slideshow":{"slide_type":"slide"},"id":"BA05EB96C4124939BFBC1660DDC3398C","trusted":true,"collapsed":false,"scrolled":false},"outputs":[{"output_type":"execute_result","metadata":{},"data":{"text/plain":"              PPI  PPI_Curent_Month_YOY   PPI_Total\ncount  182.000000            182.000000  182.000000\nmean   100.990989              0.009904  101.087582\nstd      4.330844              0.043301    4.231262\nmin     91.780000             -0.082200   93.500000\n25%     97.900000             -0.021000   98.002500\n50%    100.500000              0.005000  100.250000\n75%    104.495000              0.044950  105.122500\nmax    110.060000              0.100600  108.340000","text/html":"<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>PPI</th>\n      <th>PPI_Curent_Month_YOY</th>\n      <th>PPI_Total</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <td>count</td>\n      <td>182.000000</td>\n      <td>182.000000</td>\n      <td>182.000000</td>\n    </tr>\n    <tr>\n      <td>mean</td>\n      <td>100.990989</td>\n      <td>0.009904</td>\n      <td>101.087582</td>\n    </tr>\n    <tr>\n      <td>std</td>\n      <td>4.330844</td>\n      <td>0.043301</td>\n      <td>4.231262</td>\n    </tr>\n    <tr>\n      <td>min</td>\n      <td>91.780000</td>\n      <td>-0.082200</td>\n      <td>93.500000</td>\n    </tr>\n    <tr>\n      <td>25%</td>\n      <td>97.900000</td>\n      <td>-0.021000</td>\n      <td>98.002500</td>\n    </tr>\n    <tr>\n      <td>50%</td>\n      <td>100.500000</td>\n      <td>0.005000</td>\n      <td>100.250000</td>\n    </tr>\n    <tr>\n      <td>75%</td>\n      <td>104.495000</td>\n      <td>0.044950</td>\n      <td>105.122500</td>\n    </tr>\n    <tr>\n      <td>max</td>\n      <td>110.060000</td>\n      <td>0.100600</td>\n      <td>108.340000</td>\n    </tr>\n  </tbody>\n</table>\n</div>"},"execution_count":11}],"source":"PPI.describe()"},{"cell_type":"code","execution_count":12,"id":"appointed-fraud","metadata":{"jupyter":{},"tags":[],"slideshow":{"slide_type":"slide"},"id":"C9A2CCADBE0649F8A94260A14591163B","trusted":true,"collapsed":false,"scrolled":false},"outputs":[],"source":"def expolt_show(clear_data):\n    fig=4\n    row=len(clear_data.columns)\n    plt.figure(figsize=(5*fig,4*row),dpi=150)\n    i=0\n    for col in clear_data.columns[1:]:\n        i+=1\n        ax = plt.subplot(4,3,i)\n        sns.lineplot(x=\"Month\",y=col,data=clear_data)\n    plt.show()"},{"cell_type":"code","execution_count":13,"id":"balanced-jacksonville","metadata":{"jupyter":{},"tags":[],"slideshow":{"slide_type":"slide"},"id":"2E54324DF7ED407385588A4068BBB33A","trusted":true,"collapsed":false,"scrolled":false},"outputs":[{"output_type":"display_data","metadata":{"needs_background":"light"},"data":{"text/plain":"<Figure size 3000x2400 with 3 Axes>","text/html":"<img src=\"https://cdn.kesci.com/upload/rt/2E54324DF7ED407385588A4068BBB33A/qtwsq34hhz.png\">"}}],"source":"expolt_show(PPI)"},{"cell_type":"code","execution_count":14,"id":"comparable-spiritual","metadata":{"jupyter":{},"tags":[],"slideshow":{"slide_type":"slide"},"id":"74B99400BFAE48A680ACC29777D4BBCC","trusted":true,"collapsed":false,"scrolled":false},"outputs":[],"source":"PMI[\"Month\"]=pd.to_datetime(PMI[\"Montth\"],format=\"%Y年%m月份\")\nPMI[\"Manufacturing_YOY\"]=PMI[\"Manufacturing_YOY\"].apply(clear_percent)\nPMI[\"Nonmanufacturing_YOY\"]=PMI[\"Nonmanufacturing_YOY\"].apply(clear_percent)"},{"cell_type":"code","execution_count":15,"id":"sound-enhancement","metadata":{"jupyter":{},"tags":[],"slideshow":{"slide_type":"slide"},"id":"52296742174F4948928DE90B797D23F7","trusted":true,"collapsed":false,"scrolled":false},"outputs":[{"output_type":"execute_result","metadata":{},"data":{"text/plain":"       Manufacturing_Industry_Index  Manufacturing_YOY  \\\ncount                    158.000000         158.000000   \nmean                      50.986076          -0.002117   \nstd                        2.583373           0.082870   \nmin                       35.700000          -0.299600   \n25%                       50.100000          -0.027375   \n50%                       50.900000          -0.004000   \n75%                       51.775000           0.021000   \nmax                       59.200000           0.422700   \n\n       Nonmanufacturing_Industry_Index  Nonmanufacturing_YOY  \ncount                       158.000000            158.000000  \nmean                         54.995570             -0.005294  \nstd                           2.728602              0.081858  \nmin                          29.600000             -0.454900  \n25%                          53.825000             -0.026025  \n50%                          54.650000             -0.003650  \n75%                          56.200000              0.009300  \nmax                          60.200000              0.736500  ","text/html":"<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>Manufacturing_Industry_Index</th>\n      <th>Manufacturing_YOY</th>\n      <th>Nonmanufacturing_Industry_Index</th>\n      <th>Nonmanufacturing_YOY</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <td>count</td>\n      <td>158.000000</td>\n      <td>158.000000</td>\n      <td>158.000000</td>\n      <td>158.000000</td>\n    </tr>\n    <tr>\n      <td>mean</td>\n      <td>50.986076</td>\n      <td>-0.002117</td>\n      <td>54.995570</td>\n      <td>-0.005294</td>\n    </tr>\n    <tr>\n      <td>std</td>\n      <td>2.583373</td>\n      <td>0.082870</td>\n      <td>2.728602</td>\n      <td>0.081858</td>\n    </tr>\n    <tr>\n      <td>min</td>\n      <td>35.700000</td>\n      <td>-0.299600</td>\n      <td>29.600000</td>\n      <td>-0.454900</td>\n    </tr>\n    <tr>\n      <td>25%</td>\n      <td>50.100000</td>\n      <td>-0.027375</td>\n      <td>53.825000</td>\n      <td>-0.026025</td>\n    </tr>\n    <tr>\n      <td>50%</td>\n      <td>50.900000</td>\n      <td>-0.004000</td>\n      <td>54.650000</td>\n      <td>-0.003650</td>\n    </tr>\n    <tr>\n      <td>75%</td>\n      <td>51.775000</td>\n      <td>0.021000</td>\n      <td>56.200000</td>\n      <td>0.009300</td>\n    </tr>\n    <tr>\n      <td>max</td>\n      <td>59.200000</td>\n      <td>0.422700</td>\n      <td>60.200000</td>\n      <td>0.736500</td>\n    </tr>\n  </tbody>\n</table>\n</div>"},"execution_count":15}],"source":"PMI.describe()"},{"cell_type":"code","execution_count":16,"id":"eligible-reviewer","metadata":{"jupyter":{},"tags":[],"slideshow":{"slide_type":"slide"},"id":"8AB5904DFAEB4BE6B9A8FDCA105CDA70","trusted":true,"collapsed":false,"scrolled":false},"outputs":[{"output_type":"execute_result","metadata":{},"data":{"text/plain":"      Montth  Manufacturing_Industry_Index  Manufacturing_YOY  \\\n0  2021年02月份                          50.6             0.4174   \n1  2021年01月份                          51.3             0.0260   \n2  2020年12月份                          51.9             0.0339   \n3  2020年11月份                          52.1             0.0378   \n4  2020年10月份                          51.4             0.0426   \n\n   Nonmanufacturing_Industry_Index  Nonmanufacturing_YOY      Month  \n0                             51.4                0.7365 2021-02-01  \n1                             52.4               -0.0314 2021-01-01  \n2                             55.7                0.0411 2020-12-01  \n3                             56.4                0.0368 2020-11-01  \n4                             56.2                0.0644 2020-10-01  ","text/html":"<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>Montth</th>\n      <th>Manufacturing_Industry_Index</th>\n      <th>Manufacturing_YOY</th>\n      <th>Nonmanufacturing_Industry_Index</th>\n      <th>Nonmanufacturing_YOY</th>\n      <th>Month</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <td>0</td>\n      <td>2021年02月份</td>\n      <td>50.6</td>\n      <td>0.4174</td>\n      <td>51.4</td>\n      <td>0.7365</td>\n      <td>2021-02-01</td>\n    </tr>\n    <tr>\n      <td>1</td>\n      <td>2021年01月份</td>\n      <td>51.3</td>\n      <td>0.0260</td>\n      <td>52.4</td>\n      <td>-0.0314</td>\n      <td>2021-01-01</td>\n    </tr>\n    <tr>\n      <td>2</td>\n      <td>2020年12月份</td>\n      <td>51.9</td>\n      <td>0.0339</td>\n      <td>55.7</td>\n      <td>0.0411</td>\n      <td>2020-12-01</td>\n    </tr>\n    <tr>\n      <td>3</td>\n      <td>2020年11月份</td>\n      <td>52.1</td>\n      <td>0.0378</td>\n      <td>56.4</td>\n      <td>0.0368</td>\n      <td>2020-11-01</td>\n    </tr>\n    <tr>\n      <td>4</td>\n      <td>2020年10月份</td>\n      <td>51.4</td>\n      <td>0.0426</td>\n      <td>56.2</td>\n      <td>0.0644</td>\n      <td>2020-10-01</td>\n    </tr>\n  </tbody>\n</table>\n</div>"},"execution_count":16}],"source":"PMI.head()"},{"cell_type":"code","execution_count":19,"id":"honey-hunter","metadata":{"jupyter":{},"tags":[],"slideshow":{"slide_type":"slide"},"id":"B6145642B85A458B8DF97F7AA8E08FA0","trusted":true,"collapsed":true,"scrolled":false},"outputs":[{"output_type":"error","ename":"KeyError","evalue":"\"['Montth'] not found in axis\"","traceback":["\u001b[0;31m---------------------------------------------------------------------------\u001b[0m","\u001b[0;31mKeyError\u001b[0m                                  Traceback (most recent call last)","\u001b[0;32m<ipython-input-19-797ebeb64543>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mPMI\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mPMI\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdrop\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"Montth\"\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0maxis\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m","\u001b[0;32m/opt/conda/lib/python3.7/site-packages/pandas/core/frame.py\u001b[0m in \u001b[0;36mdrop\u001b[0;34m(self, labels, axis, index, columns, level, inplace, errors)\u001b[0m\n\u001b[1;32m   4100\u001b[0m             \u001b[0mlevel\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mlevel\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   4101\u001b[0m             \u001b[0minplace\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0minplace\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 4102\u001b[0;31m             \u001b[0merrors\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0merrors\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   4103\u001b[0m         )\n\u001b[1;32m   4104\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n","\u001b[0;32m/opt/conda/lib/python3.7/site-packages/pandas/core/generic.py\u001b[0m in \u001b[0;36mdrop\u001b[0;34m(self, labels, axis, index, columns, level, inplace, errors)\u001b[0m\n\u001b[1;32m   3912\u001b[0m         \u001b[0;32mfor\u001b[0m \u001b[0maxis\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlabels\u001b[0m \u001b[0;32min\u001b[0m \u001b[0maxes\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mitems\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   3913\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mlabels\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 3914\u001b[0;31m                 \u001b[0mobj\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mobj\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_drop_axis\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlabels\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlevel\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mlevel\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0merrors\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0merrors\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   3915\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   3916\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0minplace\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n","\u001b[0;32m/opt/conda/lib/python3.7/site-packages/pandas/core/generic.py\u001b[0m in \u001b[0;36m_drop_axis\u001b[0;34m(self, labels, axis, level, errors)\u001b[0m\n\u001b[1;32m   3944\u001b[0m                 \u001b[0mnew_axis\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0maxis\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdrop\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlabels\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlevel\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mlevel\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0merrors\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0merrors\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   3945\u001b[0m             \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 3946\u001b[0;31m                 \u001b[0mnew_axis\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0maxis\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdrop\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlabels\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0merrors\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0merrors\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   3947\u001b[0m             \u001b[0mresult\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mreindex\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m**\u001b[0m\u001b[0;34m{\u001b[0m\u001b[0maxis_name\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mnew_axis\u001b[0m\u001b[0;34m}\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   3948\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n","\u001b[0;32m/opt/conda/lib/python3.7/site-packages/pandas/core/indexes/base.py\u001b[0m in \u001b[0;36mdrop\u001b[0;34m(self, labels, errors)\u001b[0m\n\u001b[1;32m   5338\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mmask\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0many\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   5339\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0merrors\u001b[0m \u001b[0;34m!=\u001b[0m \u001b[0;34m\"ignore\"\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 5340\u001b[0;31m                 \u001b[0;32mraise\u001b[0m \u001b[0mKeyError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"{} not found in axis\"\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mformat\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlabels\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mmask\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   5341\u001b[0m             \u001b[0mindexer\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mindexer\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m~\u001b[0m\u001b[0mmask\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   5342\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdelete\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mindexer\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n","\u001b[0;31mKeyError\u001b[0m: \"['Montth'] not found in axis\""]}],"source":"PMI=PMI.drop(\"Montth\",axis=1)"},{"metadata":{"id":"2A94A5251E0C445A811F713B32398CD4","notebookId":"60b33cef4223f3001719a7b1","jupyter":{},"tags":[],"slideshow":{"slide_type":"slide"},"trusted":true,"collapsed":false,"scrolled":false},"cell_type":"code","outputs":[{"output_type":"stream","text":"<class 'pandas.core.frame.DataFrame'>\nRangeIndex: 158 entries, 0 to 157\nData columns (total 5 columns):\nManufacturing_Industry_Index       158 non-null float64\nManufacturing_YOY                  158 non-null float64\nNonmanufacturing_Industry_Index    158 non-null float64\nNonmanufacturing_YOY               158 non-null float64\nMonth                              158 non-null datetime64[ns]\ndtypes: datetime64[ns](1), float64(4)\nmemory usage: 6.3 KB\n","name":"stdout"}],"source":"PMI.info()","execution_count":20},{"cell_type":"code","execution_count":22,"id":"basic-industry","metadata":{"jupyter":{},"tags":[],"slideshow":{"slide_type":"slide"},"id":"0B6917A1B7A74ED28C3D5C946BD88722","trusted":true,"collapsed":false,"scrolled":false},"outputs":[{"output_type":"execute_result","metadata":{},"data":{"text/plain":"       Month  Current_Month_FDI FDI_YOY FDI_Comparative_Rate FDI_Total  \\\n0  2020年10月份              118.3  18.32%              -17.04%         -   \n1  2020年09月份              142.6  23.78%               18.64%         -   \n2  2020年08月份              120.2  14.92%                    -         -   \n3  2020年05月份               98.7   4.20%                    -         -   \n4  2020年01月份              126.8   2.20%                    -         -   \n\n  FDI_Total_YOY  \n0             -  \n1             -  \n2             -  \n3             -  \n4             -  ","text/html":"<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>Month</th>\n      <th>Current_Month_FDI</th>\n      <th>FDI_YOY</th>\n      <th>FDI_Comparative_Rate</th>\n      <th>FDI_Total</th>\n      <th>FDI_Total_YOY</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <td>0</td>\n      <td>2020年10月份</td>\n      <td>118.3</td>\n      <td>18.32%</td>\n      <td>-17.04%</td>\n      <td>-</td>\n      <td>-</td>\n    </tr>\n    <tr>\n      <td>1</td>\n      <td>2020年09月份</td>\n      <td>142.6</td>\n      <td>23.78%</td>\n      <td>18.64%</td>\n      <td>-</td>\n      <td>-</td>\n    </tr>\n    <tr>\n      <td>2</td>\n      <td>2020年08月份</td>\n      <td>120.2</td>\n      <td>14.92%</td>\n      <td>-</td>\n      <td>-</td>\n      <td>-</td>\n    </tr>\n    <tr>\n      <td>3</td>\n      <td>2020年05月份</td>\n      <td>98.7</td>\n      <td>4.20%</td>\n      <td>-</td>\n      <td>-</td>\n      <td>-</td>\n    </tr>\n    <tr>\n      <td>4</td>\n      <td>2020年01月份</td>\n      <td>126.8</td>\n      <td>2.20%</td>\n      <td>-</td>\n      <td>-</td>\n      <td>-</td>\n    </tr>\n  </tbody>\n</table>\n</div>"},"execution_count":22}],"source":"FDI.columns=[\"Month\",\"Current_Month_FDI\",\"FDI_YOY\",\"FDI_Comparative_Rate\",\"FDI_Total\",\"FDI_Total_YOY\"]\nFDI.head()"},{"cell_type":"code","execution_count":23,"id":"planned-psychology","metadata":{"jupyter":{},"tags":[],"slideshow":{"slide_type":"slide"},"id":"43F2039EC4E34B068D307ED991047676","trusted":true,"collapsed":false,"scrolled":false},"outputs":[],"source":"FDI=FDI.replace(\"-\",\"NaN\")\ncleardata(FDI)\nFDI[\"FDI_YOY\"]=FDI[\"FDI_YOY\"].astype(\"float\")"},{"cell_type":"code","execution_count":24,"id":"backed-england","metadata":{"jupyter":{},"tags":[],"slideshow":{"slide_type":"slide"},"id":"EBC7D294E80A4935AA8964E459731C0B","trusted":true,"scrolled":false,"collapsed":false},"outputs":[{"output_type":"stream","text":"<class 'pandas.core.frame.DataFrame'>\nRangeIndex: 148 entries, 0 to 147\nData columns (total 6 columns):\nMonth                   148 non-null datetime64[ns]\nCurrent_Month_FDI       148 non-null float64\nFDI_YOY                 148 non-null float64\nFDI_Comparative_Rate    145 non-null float64\nFDI_Total               148 non-null object\nFDI_Total_YOY           143 non-null float64\ndtypes: datetime64[ns](1), float64(4), object(1)\nmemory usage: 7.1+ KB\n","name":"stdout"}],"source":"FDI.info()"},{"cell_type":"code","execution_count":25,"id":"durable-crack","metadata":{"jupyter":{},"tags":[],"slideshow":{"slide_type":"slide"},"id":"23B56DC18C4943E8BB7B5B54AE7C7BAA","trusted":true,"collapsed":false,"scrolled":false},"outputs":[{"output_type":"execute_result","metadata":{},"data":{"text/plain":"       Current_Month_FDI     FDI_YOY  FDI_Comparative_Rate  FDI_Total_YOY\ncount         148.000000  148.000000             145.00000     143.000000\nmean           98.403041    0.077656               0.05068       0.068103\nstd            24.946796    0.223165               0.32794       0.173612\nmin            53.220000   -0.543500              -0.56120      -0.321700\n25%            82.935000   -0.008250              -0.20730      -0.013400\n50%            91.520000    0.029300               0.01250       0.036000\n75%           118.672500    0.124175               0.26630       0.101350\nmax           187.870000    1.164000               1.08000       1.052000","text/html":"<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>Current_Month_FDI</th>\n      <th>FDI_YOY</th>\n      <th>FDI_Comparative_Rate</th>\n      <th>FDI_Total_YOY</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <td>count</td>\n      <td>148.000000</td>\n      <td>148.000000</td>\n      <td>145.00000</td>\n      <td>143.000000</td>\n    </tr>\n    <tr>\n      <td>mean</td>\n      <td>98.403041</td>\n      <td>0.077656</td>\n      <td>0.05068</td>\n      <td>0.068103</td>\n    </tr>\n    <tr>\n      <td>std</td>\n      <td>24.946796</td>\n      <td>0.223165</td>\n      <td>0.32794</td>\n      <td>0.173612</td>\n    </tr>\n    <tr>\n      <td>min</td>\n      <td>53.220000</td>\n      <td>-0.543500</td>\n      <td>-0.56120</td>\n      <td>-0.321700</td>\n    </tr>\n    <tr>\n      <td>25%</td>\n      <td>82.935000</td>\n      <td>-0.008250</td>\n      <td>-0.20730</td>\n      <td>-0.013400</td>\n    </tr>\n    <tr>\n      <td>50%</td>\n      <td>91.520000</td>\n      <td>0.029300</td>\n      <td>0.01250</td>\n      <td>0.036000</td>\n    </tr>\n    <tr>\n      <td>75%</td>\n      <td>118.672500</td>\n      <td>0.124175</td>\n      <td>0.26630</td>\n      <td>0.101350</td>\n    </tr>\n    <tr>\n      <td>max</td>\n      <td>187.870000</td>\n      <td>1.164000</td>\n      <td>1.08000</td>\n      <td>1.052000</td>\n    </tr>\n  </tbody>\n</table>\n</div>"},"execution_count":25}],"source":"FDI.describe()"},{"cell_type":"code","execution_count":26,"id":"molecular-bracelet","metadata":{"jupyter":{},"tags":[],"slideshow":{"slide_type":"slide"},"id":"AD1B3CCEED7E40838463CAC7A677DA27","trusted":true,"collapsed":false,"scrolled":false},"outputs":[{"output_type":"execute_result","metadata":{},"data":{"text/plain":"       Month  Current_Month_FDI  FDI_YOY  FDI_Comparative_Rate FDI_Total  \\\n0 2020-10-01              118.3   0.1832               -0.1704       NaN   \n1 2020-09-01              142.6   0.2378                0.1864       NaN   \n2 2020-08-01              120.2   0.1492                   NaN       NaN   \n3 2020-05-01               98.7   0.0420                   NaN       NaN   \n4 2020-01-01              126.8   0.0220                   NaN       NaN   \n\n   FDI_Total_YOY  \n0            NaN  \n1            NaN  \n2            NaN  \n3            NaN  \n4            NaN  ","text/html":"<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>Month</th>\n      <th>Current_Month_FDI</th>\n      <th>FDI_YOY</th>\n      <th>FDI_Comparative_Rate</th>\n      <th>FDI_Total</th>\n      <th>FDI_Total_YOY</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <td>0</td>\n      <td>2020-10-01</td>\n      <td>118.3</td>\n      <td>0.1832</td>\n      <td>-0.1704</td>\n      <td>NaN</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <td>1</td>\n      <td>2020-09-01</td>\n      <td>142.6</td>\n      <td>0.2378</td>\n      <td>0.1864</td>\n      <td>NaN</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <td>2</td>\n      <td>2020-08-01</td>\n      <td>120.2</td>\n      <td>0.1492</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <td>3</td>\n      <td>2020-05-01</td>\n      <td>98.7</td>\n      <td>0.0420</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <td>4</td>\n      <td>2020-01-01</td>\n      <td>126.8</td>\n      <td>0.0220</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n    </tr>\n  </tbody>\n</table>\n</div>"},"execution_count":26}],"source":"FDI.head()"},{"cell_type":"code","execution_count":27,"id":"incomplete-overhead","metadata":{"jupyter":{},"tags":[],"slideshow":{"slide_type":"slide"},"id":"001B3C51DC6C43DA99302A6BDE6F2BFE","trusted":true,"collapsed":false,"scrolled":false},"outputs":[{"output_type":"stream","text":"<class 'pandas.core.frame.DataFrame'>\nRangeIndex: 148 entries, 0 to 147\nData columns (total 6 columns):\nMonth                   148 non-null datetime64[ns]\nCurrent_Month_FDI       148 non-null float64\nFDI_YOY                 148 non-null float64\nFDI_Comparative_Rate    145 non-null float64\nFDI_Total               148 non-null object\nFDI_Total_YOY           143 non-null float64\ndtypes: datetime64[ns](1), float64(4), object(1)\nmemory usage: 7.1+ KB\n","name":"stdout"}],"source":"FDI.info()"},{"cell_type":"code","execution_count":29,"id":"identical-sixth","metadata":{"jupyter":{},"tags":[],"slideshow":{"slide_type":"slide"},"id":"A604DF3C271E43238D0390A9ED900EA8","trusted":true,"collapsed":false,"scrolled":false},"outputs":[{"output_type":"execute_result","metadata":{},"data":{"text/plain":"                                  0          1          2          3  \\\nMonth                     2021年02月份  2021年01月份  2020年12月份  2020年11月份   \nNation_Current_Month           99.8       99.7      100.2       99.5   \nNation_YOY                   -0.20%     -0.30%      0.20%     -0.50%   \nNation_Comparative_Rate       0.60%      1.00%      0.70%     -0.60%   \nNation_Total                   99.7       99.7      102.5      102.7   \nCity_Current_Month             99.8       99.6      100.2       99.6   \nCity_YOY                     -0.20%     -0.40%      0.20%     -0.40%   \nCity_Comparative_Rate         0.60%      1.00%      0.70%     -0.60%   \nCity_Total                     99.7       99.6      102.3      102.5   \nCountry_Current_Month          99.9       99.9      100.2       99.2   \nCountry_YOY                  -0.10%     -0.10%      0.20%     -0.80%   \nCountry_Comparative_Rate      0.40%      1.10%      0.90%     -0.60%   \nCountry_Total                  99.9       99.9        103      103.3   \n\n                                  4  \nMonth                     2020年10月份  \nNation_Current_Month          100.5  \nNation_YOY                    0.50%  \nNation_Comparative_Rate      -0.30%  \nNation_Total                    103  \nCity_Current_Month            100.5  \nCity_YOY                      0.50%  \nCity_Comparative_Rate        -0.30%  \nCity_Total                    102.8  \nCountry_Current_Month         100.4  \nCountry_YOY                   0.40%  \nCountry_Comparative_Rate     -0.50%  \nCountry_Total                 103.7  ","text/html":"<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>0</th>\n      <th>1</th>\n      <th>2</th>\n      <th>3</th>\n      <th>4</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <td>Month</td>\n      <td>2021年02月份</td>\n      <td>2021年01月份</td>\n      <td>2020年12月份</td>\n      <td>2020年11月份</td>\n      <td>2020年10月份</td>\n    </tr>\n    <tr>\n      <td>Nation_Current_Month</td>\n      <td>99.8</td>\n      <td>99.7</td>\n      <td>100.2</td>\n      <td>99.5</td>\n      <td>100.5</td>\n    </tr>\n    <tr>\n      <td>Nation_YOY</td>\n      <td>-0.20%</td>\n      <td>-0.30%</td>\n      <td>0.20%</td>\n      <td>-0.50%</td>\n      <td>0.50%</td>\n    </tr>\n    <tr>\n      <td>Nation_Comparative_Rate</td>\n      <td>0.60%</td>\n      <td>1.00%</td>\n      <td>0.70%</td>\n      <td>-0.60%</td>\n      <td>-0.30%</td>\n    </tr>\n    <tr>\n      <td>Nation_Total</td>\n      <td>99.7</td>\n      <td>99.7</td>\n      <td>102.5</td>\n      <td>102.7</td>\n      <td>103</td>\n    </tr>\n    <tr>\n      <td>City_Current_Month</td>\n      <td>99.8</td>\n      <td>99.6</td>\n      <td>100.2</td>\n      <td>99.6</td>\n      <td>100.5</td>\n    </tr>\n    <tr>\n      <td>City_YOY</td>\n      <td>-0.20%</td>\n      <td>-0.40%</td>\n      <td>0.20%</td>\n      <td>-0.40%</td>\n      <td>0.50%</td>\n    </tr>\n    <tr>\n      <td>City_Comparative_Rate</td>\n      <td>0.60%</td>\n      <td>1.00%</td>\n      <td>0.70%</td>\n      <td>-0.60%</td>\n      <td>-0.30%</td>\n    </tr>\n    <tr>\n      <td>City_Total</td>\n      <td>99.7</td>\n      <td>99.6</td>\n      <td>102.3</td>\n      <td>102.5</td>\n      <td>102.8</td>\n    </tr>\n    <tr>\n      <td>Country_Current_Month</td>\n      <td>99.9</td>\n      <td>99.9</td>\n      <td>100.2</td>\n      <td>99.2</td>\n      <td>100.4</td>\n    </tr>\n    <tr>\n      <td>Country_YOY</td>\n      <td>-0.10%</td>\n      <td>-0.10%</td>\n      <td>0.20%</td>\n      <td>-0.80%</td>\n      <td>0.40%</td>\n    </tr>\n    <tr>\n      <td>Country_Comparative_Rate</td>\n      <td>0.40%</td>\n      <td>1.10%</td>\n      <td>0.90%</td>\n      <td>-0.60%</td>\n      <td>-0.50%</td>\n    </tr>\n    <tr>\n      <td>Country_Total</td>\n      <td>99.9</td>\n      <td>99.9</td>\n      <td>103</td>\n      <td>103.3</td>\n      <td>103.7</td>\n    </tr>\n  </tbody>\n</table>\n</div>"},"execution_count":29}],"source":"CPI.head().T\n# CPI.columns=[\"Month\",\"CPI_Nation_Current_Month\",\"CPI_Nation_YOY\",\"Nation_Comparative_Rate\",\"Nation_Total\",\"City_Current_Month\",\"City_YOY\",\"\",\"\"]"},{"cell_type":"code","execution_count":30,"id":"reflected-basic","metadata":{"jupyter":{},"tags":[],"slideshow":{"slide_type":"slide"},"id":"73690EFE1E154B5E8E15978F192AA939","trusted":true,"collapsed":false,"scrolled":false},"outputs":[],"source":"cleardata(CPI)"},{"cell_type":"code","execution_count":31,"id":"satisfied-blast","metadata":{"jupyter":{},"tags":[],"slideshow":{"slide_type":"slide"},"id":"E62C4927B5B34414B382A41186D0E0B4","trusted":true,"collapsed":false,"scrolled":false},"outputs":[{"output_type":"display_data","metadata":{"needs_background":"light"},"data":{"text/plain":"<Figure size 3000x7800 with 12 Axes>","text/html":"<img src=\"https://cdn.kesci.com/upload/rt/E62C4927B5B34414B382A41186D0E0B4/qtwszcwpki.png\">"}}],"source":"expolt_show(CPI)"},{"cell_type":"code","execution_count":32,"id":"usual-private","metadata":{"jupyter":{},"tags":[],"slideshow":{"slide_type":"slide"},"id":"9AC876376E704E53AAD7CAC396A8F11F","trusted":true,"collapsed":false,"scrolled":false},"outputs":[{"output_type":"execute_result","metadata":{},"data":{"text/plain":"      Quarter  Tax_Revenue_Total      YOY Quarterly_Comparative_Rate\n0  2020年1-4季度           154310.0   -2.30%                     -0.04%\n1  2020年1-3季度           118876.0   -6.40%                     -0.14%\n2  2020年1-2季度            81990.0  -11.30%                      4.47%\n3    2020年1季度            39029.0  -16.40%                     -0.75%\n4  2019年1-4季度           157992.0    1.00%                     -0.10%","text/html":"<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>Quarter</th>\n      <th>Tax_Revenue_Total</th>\n      <th>YOY</th>\n      <th>Quarterly_Comparative_Rate</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <td>0</td>\n      <td>2020年1-4季度</td>\n      <td>154310.0</td>\n      <td>-2.30%</td>\n      <td>-0.04%</td>\n    </tr>\n    <tr>\n      <td>1</td>\n      <td>2020年1-3季度</td>\n      <td>118876.0</td>\n      <td>-6.40%</td>\n      <td>-0.14%</td>\n    </tr>\n    <tr>\n      <td>2</td>\n      <td>2020年1-2季度</td>\n      <td>81990.0</td>\n      <td>-11.30%</td>\n      <td>4.47%</td>\n    </tr>\n    <tr>\n      <td>3</td>\n      <td>2020年1季度</td>\n      <td>39029.0</td>\n      <td>-16.40%</td>\n      <td>-0.75%</td>\n    </tr>\n    <tr>\n      <td>4</td>\n      <td>2019年1-4季度</td>\n      <td>157992.0</td>\n      <td>1.00%</td>\n      <td>-0.10%</td>\n    </tr>\n  </tbody>\n</table>\n</div>"},"execution_count":32}],"source":"Nation_tax.head()"},{"cell_type":"code","execution_count":33,"id":"recovered-validity","metadata":{"jupyter":{},"tags":[],"slideshow":{"slide_type":"slide"},"id":"8AB90846C67F435C97CB285892AF5999","trusted":true,"collapsed":false,"scrolled":false},"outputs":[],"source":"RMB_Foreg.head()\nRMB_Foreg.columns=[\"Month\",\"RMB_Fore_Current_Month_Value\",\"RMB_fore_YOY\",\"RMB_Comparative_Rate\",\"RMB_Total\"]"},{"cell_type":"code","execution_count":34,"id":"seventh-directory","metadata":{"jupyter":{},"tags":[],"slideshow":{"slide_type":"slide"},"id":"A579101FFB8E4D358563198D9862325B","trusted":true,"collapsed":false,"scrolled":false},"outputs":[],"source":"cleardata(RMB_Foreg)"},{"cell_type":"code","execution_count":35,"id":"younger-great","metadata":{"jupyter":{},"tags":[],"slideshow":{"slide_type":"slide"},"id":"CECE078590CA41E2AF3FCB5BEC57045D","trusted":true,"collapsed":false,"scrolled":false},"outputs":[{"output_type":"display_data","metadata":{"needs_background":"light"},"data":{"text/plain":"<Figure size 3000x3000 with 4 Axes>","text/html":"<img src=\"https://cdn.kesci.com/upload/rt/CECE078590CA41E2AF3FCB5BEC57045D/qtwszonu2b.png\">"}}],"source":"expolt_show(RMB_Foreg)"},{"cell_type":"code","execution_count":36,"id":"regulation-article","metadata":{"jupyter":{},"tags":[],"slideshow":{"slide_type":"slide"},"id":"682ECF0A131F4DF08B0F8B766C430224","trusted":true,"collapsed":false,"scrolled":false},"outputs":[{"output_type":"execute_result","metadata":{},"data":{"text/plain":"       Month  Current_Month_Value Current_Month_YOY  \\\n0  2021年02月份                  0.0             0.00%   \n1  2020年12月份              13406.0            17.44%   \n2  2020年11月份              10956.0            -2.73%   \n3  2020年10月份              17531.0             2.97%   \n4  2020年09月份              14234.0             4.53%   \n\n  Current_Month_Comparattive     Total Total_YOY  \n0                      0.00%   41805.0    18.70%  \n1                     22.36%  182895.0    -3.90%  \n2                    -37.50%  169489.0    -5.30%  \n3                     23.16%  158533.0    -5.50%  \n4                     18.19%  141002.0    -6.40%  ","text/html":"<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>Month</th>\n      <th>Current_Month_Value</th>\n      <th>Current_Month_YOY</th>\n      <th>Current_Month_Comparattive</th>\n      <th>Total</th>\n      <th>Total_YOY</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <td>0</td>\n      <td>2021年02月份</td>\n      <td>0.0</td>\n      <td>0.00%</td>\n      <td>0.00%</td>\n      <td>41805.0</td>\n      <td>18.70%</td>\n    </tr>\n    <tr>\n      <td>1</td>\n      <td>2020年12月份</td>\n      <td>13406.0</td>\n      <td>17.44%</td>\n      <td>22.36%</td>\n      <td>182895.0</td>\n      <td>-3.90%</td>\n    </tr>\n    <tr>\n      <td>2</td>\n      <td>2020年11月份</td>\n      <td>10956.0</td>\n      <td>-2.73%</td>\n      <td>-37.50%</td>\n      <td>169489.0</td>\n      <td>-5.30%</td>\n    </tr>\n    <tr>\n      <td>3</td>\n      <td>2020年10月份</td>\n      <td>17531.0</td>\n      <td>2.97%</td>\n      <td>23.16%</td>\n      <td>158533.0</td>\n      <td>-5.50%</td>\n    </tr>\n    <tr>\n      <td>4</td>\n      <td>2020年09月份</td>\n      <td>14234.0</td>\n      <td>4.53%</td>\n      <td>18.19%</td>\n      <td>141002.0</td>\n      <td>-6.40%</td>\n    </tr>\n  </tbody>\n</table>\n</div>"},"execution_count":36}],"source":"Fiscal_Re.head()"},{"cell_type":"code","execution_count":37,"id":"previous-asbestos","metadata":{"jupyter":{},"tags":[],"slideshow":{"slide_type":"slide"},"id":"89C89D3CC63942A3AB05C10CF47F5608","trusted":true,"collapsed":false,"scrolled":false},"outputs":[],"source":"cleardata(Fiscal_Re)"},{"cell_type":"code","execution_count":38,"id":"quarterly-tolerance","metadata":{"jupyter":{},"tags":[],"slideshow":{"slide_type":"slide"},"id":"FF5557AE5005479D818D087BFB6AB015","trusted":true,"collapsed":false,"scrolled":false},"outputs":[],"source":"Fiscal_Re[\"Current_Month_Comparattive\"]=Fiscal_Re[\"Current_Month_Comparattive\"].apply(lambda x:(float(x[:-1])/100))"},{"cell_type":"code","execution_count":39,"id":"little-monte","metadata":{"jupyter":{},"tags":[],"slideshow":{"slide_type":"slide"},"id":"07E49949407E42F28553670BC09E2E56","trusted":true,"collapsed":false,"scrolled":false},"outputs":[],"source":"Fiscal_Re.columns=[\"Month\",\"Fis_Current_Month_Value\",\"Fis_Current_Month_YOY\",\"Fis_Current_Month_Comparattive\",\"Fis_Total\",\"Fis_Total_YOY\"]"},{"cell_type":"code","execution_count":40,"id":"characteristic-vacation","metadata":{"jupyter":{},"tags":[],"slideshow":{"slide_type":"slide"},"id":"20B9EED4F02F46EB87B1246CE6C9C47B","trusted":true,"collapsed":false,"scrolled":false},"outputs":[{"output_type":"display_data","metadata":{"needs_background":"light"},"data":{"text/plain":"<Figure size 3000x3600 with 5 Axes>","text/html":"<img src=\"https://cdn.kesci.com/upload/rt/20B9EED4F02F46EB87B1246CE6C9C47B/qtwszw3m7t.png\">"}}],"source":"expolt_show(Fiscal_Re)"},{"cell_type":"code","execution_count":41,"id":"colored-validation","metadata":{"jupyter":{},"tags":[],"slideshow":{"slide_type":"slide"},"id":"B37558118EE74D8C99386DB86386557D","trusted":true,"collapsed":false,"scrolled":false},"outputs":[{"output_type":"execute_result","metadata":{},"data":{"text/plain":"       Month  Fis_Current_Month_Value  Fis_Current_Month_YOY  \\\n0 2021-02-01                      0.0                 0.0000   \n1 2020-12-01                  13406.0                 0.1744   \n2 2020-11-01                  10956.0                -0.0273   \n3 2020-10-01                  17531.0                 0.0297   \n4 2020-09-01                  14234.0                 0.0453   \n\n   Fis_Current_Month_Comparattive  Fis_Total  Fis_Total_YOY  \n0                          0.0000    41805.0          0.187  \n1                          0.2236   182895.0         -0.039  \n2                         -0.3750   169489.0         -0.053  \n3                          0.2316   158533.0         -0.055  \n4                          0.1819   141002.0         -0.064  ","text/html":"<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>Month</th>\n      <th>Fis_Current_Month_Value</th>\n      <th>Fis_Current_Month_YOY</th>\n      <th>Fis_Current_Month_Comparattive</th>\n      <th>Fis_Total</th>\n      <th>Fis_Total_YOY</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <td>0</td>\n      <td>2021-02-01</td>\n      <td>0.0</td>\n      <td>0.0000</td>\n      <td>0.0000</td>\n      <td>41805.0</td>\n      <td>0.187</td>\n    </tr>\n    <tr>\n      <td>1</td>\n      <td>2020-12-01</td>\n      <td>13406.0</td>\n      <td>0.1744</td>\n      <td>0.2236</td>\n      <td>182895.0</td>\n      <td>-0.039</td>\n    </tr>\n    <tr>\n      <td>2</td>\n      <td>2020-11-01</td>\n      <td>10956.0</td>\n      <td>-0.0273</td>\n      <td>-0.3750</td>\n      <td>169489.0</td>\n      <td>-0.053</td>\n    </tr>\n    <tr>\n      <td>3</td>\n      <td>2020-10-01</td>\n      <td>17531.0</td>\n      <td>0.0297</td>\n      <td>0.2316</td>\n      <td>158533.0</td>\n      <td>-0.055</td>\n    </tr>\n    <tr>\n      <td>4</td>\n      <td>2020-09-01</td>\n      <td>14234.0</td>\n      <td>0.0453</td>\n      <td>0.1819</td>\n      <td>141002.0</td>\n      <td>-0.064</td>\n    </tr>\n  </tbody>\n</table>\n</div>"},"execution_count":41}],"source":"Fiscal_Re.head()"},{"cell_type":"code","execution_count":42,"id":"african-gamma","metadata":{"jupyter":{},"tags":[],"slideshow":{"slide_type":"slide"},"id":"88AA4835508E483B9FC92873F50CDFF5","trusted":true,"collapsed":false,"scrolled":false},"outputs":[{"output_type":"execute_result","metadata":{},"data":{"text/plain":"       Month  UF_Current_month_Value   UF_YOY UF_Comparative_Rate  \\\n0  2021年02月份                     0.0    0.00%               0.00%   \n1  2020年12月份                 19347.0    8.94%              18.93%   \n2  2020年11月份                 16268.0  -28.77%             -65.21%   \n3  2020年10月份                 46762.0   -5.87%             -18.95%   \n4  2020年09月份                 57696.0   -4.75%              16.28%   \n\n   Total_from_Beg_of_the_Year  \n0                     45236.0  \n1                    518907.0  \n2                    499560.0  \n3                    483292.0  \n4                    436530.0  ","text/html":"<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>Month</th>\n      <th>UF_Current_month_Value</th>\n      <th>UF_YOY</th>\n      <th>UF_Comparative_Rate</th>\n      <th>Total_from_Beg_of_the_Year</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <td>0</td>\n      <td>2021年02月份</td>\n      <td>0.0</td>\n      <td>0.00%</td>\n      <td>0.00%</td>\n      <td>45236.0</td>\n    </tr>\n    <tr>\n      <td>1</td>\n      <td>2020年12月份</td>\n      <td>19347.0</td>\n      <td>8.94%</td>\n      <td>18.93%</td>\n      <td>518907.0</td>\n    </tr>\n    <tr>\n      <td>2</td>\n      <td>2020年11月份</td>\n      <td>16268.0</td>\n      <td>-28.77%</td>\n      <td>-65.21%</td>\n      <td>499560.0</td>\n    </tr>\n    <tr>\n      <td>3</td>\n      <td>2020年10月份</td>\n      <td>46762.0</td>\n      <td>-5.87%</td>\n      <td>-18.95%</td>\n      <td>483292.0</td>\n    </tr>\n    <tr>\n      <td>4</td>\n      <td>2020年09月份</td>\n      <td>57696.0</td>\n      <td>-4.75%</td>\n      <td>16.28%</td>\n      <td>436530.0</td>\n    </tr>\n  </tbody>\n</table>\n</div>"},"execution_count":42}],"source":"Urban_Fixed.columns=[\"Month\",\"UF_Current_month_Value\",\"UF_YOY\",\"UF_Comparative_Rate\",\"Total_from_Beg_of_the_Year\"]\nUrban_Fixed.head()"},{"cell_type":"code","execution_count":43,"id":"unexpected-mobility","metadata":{"jupyter":{},"tags":[],"slideshow":{"slide_type":"slide"},"id":"5ADCACFF2598477ABFB24C9FCD3B57E1","trusted":true,"collapsed":false,"scrolled":false},"outputs":[],"source":"cleardata(Urban_Fixed)"},{"cell_type":"code","execution_count":44,"id":"indian-playback","metadata":{"jupyter":{},"tags":[],"slideshow":{"slide_type":"slide"},"id":"DEA1DB811ADE4C9486001A805D7CBD03","trusted":true,"collapsed":false,"scrolled":false},"outputs":[{"output_type":"display_data","metadata":{"needs_background":"light"},"data":{"text/plain":"<Figure size 3000x3000 with 4 Axes>","text/html":"<img src=\"https://cdn.kesci.com/upload/rt/DEA1DB811ADE4C9486001A805D7CBD03/qtwt03ci6r.png\">"}}],"source":"expolt_show(Urban_Fixed)"},{"cell_type":"code","execution_count":45,"id":"universal-beaver","metadata":{"jupyter":{},"tags":[],"slideshow":{"slide_type":"slide"},"id":"7B34FE2ED64447AF81B500168391EBD8","trusted":true,"collapsed":false,"scrolled":false},"outputs":[{"output_type":"execute_result","metadata":{},"data":{"text/plain":"  Announced_Date Validate_Date Before_Adjustment_Large_Financial_Institutions  \\\n0      2020年1月1日     2020年1月6日                                         13.00%   \n1      2019年9月6日    2019年9月16日                                         13.50%   \n2      2019年1月4日    2019年1月25日                                         14.00%   \n3      2019年1月4日    2019年1月15日                                         14.50%   \n4     2018年10月7日   2018年10月15日                                         15.50%   \n\n  After_Adjustment_Large_Financial_Institutions  \\\n0                                        12.50%   \n1                                        13.00%   \n2                                        13.50%   \n3                                        14.00%   \n4                                        14.50%   \n\n  Adjustment_of_Large_Financial_Institutions  \\\n0                                     -0.50%   \n1                                     -0.50%   \n2                                     -0.50%   \n3                                     -0.50%   \n4                                     -1.00%   \n\n  Before_Adjustment_Small-Medium_Financial_Institutions  \\\n0                                             11.00%      \n1                                             11.50%      \n2                                             12.00%      \n3                                             12.50%      \n4                                             13.50%      \n\n  After_Adjustment_Small-Medium_Financial_Institutions  \\\n0                                             10.50%     \n1                                             11.00%     \n2                                             11.50%     \n3                                             12.00%     \n4                                             12.50%     \n\n  Adjustment_Small-Medium_Financial_Institutions  \\\n0                                         -0.50%   \n1                                         -0.50%   \n2                                         -0.50%   \n3                                         -0.50%   \n4                                         -1.00%   \n\n  Change_of_Shanghai_Index_After_Announced  \\\n0                                    1.15%   \n1                                    0.84%   \n2                                    0.72%   \n3                                    0.72%   \n4                                   -3.72%   \n\n  Change_of_Shenzhen_Index_After_Announced  \n0                                    1.99%  \n1                                    1.82%  \n2                                    1.58%  \n3                                    1.58%  \n4                                   -4.05%  ","text/html":"<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>Announced_Date</th>\n      <th>Validate_Date</th>\n      <th>Before_Adjustment_Large_Financial_Institutions</th>\n      <th>After_Adjustment_Large_Financial_Institutions</th>\n      <th>Adjustment_of_Large_Financial_Institutions</th>\n      <th>Before_Adjustment_Small-Medium_Financial_Institutions</th>\n      <th>After_Adjustment_Small-Medium_Financial_Institutions</th>\n      <th>Adjustment_Small-Medium_Financial_Institutions</th>\n      <th>Change_of_Shanghai_Index_After_Announced</th>\n      <th>Change_of_Shenzhen_Index_After_Announced</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <td>0</td>\n      <td>2020年1月1日</td>\n      <td>2020年1月6日</td>\n      <td>13.00%</td>\n      <td>12.50%</td>\n      <td>-0.50%</td>\n      <td>11.00%</td>\n      <td>10.50%</td>\n      <td>-0.50%</td>\n      <td>1.15%</td>\n      <td>1.99%</td>\n    </tr>\n    <tr>\n      <td>1</td>\n      <td>2019年9月6日</td>\n      <td>2019年9月16日</td>\n      <td>13.50%</td>\n      <td>13.00%</td>\n      <td>-0.50%</td>\n      <td>11.50%</td>\n      <td>11.00%</td>\n      <td>-0.50%</td>\n      <td>0.84%</td>\n      <td>1.82%</td>\n    </tr>\n    <tr>\n      <td>2</td>\n      <td>2019年1月4日</td>\n      <td>2019年1月25日</td>\n      <td>14.00%</td>\n      <td>13.50%</td>\n      <td>-0.50%</td>\n      <td>12.00%</td>\n      <td>11.50%</td>\n      <td>-0.50%</td>\n      <td>0.72%</td>\n      <td>1.58%</td>\n    </tr>\n    <tr>\n      <td>3</td>\n      <td>2019年1月4日</td>\n      <td>2019年1月15日</td>\n      <td>14.50%</td>\n      <td>14.00%</td>\n      <td>-0.50%</td>\n      <td>12.50%</td>\n      <td>12.00%</td>\n      <td>-0.50%</td>\n      <td>0.72%</td>\n      <td>1.58%</td>\n    </tr>\n    <tr>\n      <td>4</td>\n      <td>2018年10月7日</td>\n      <td>2018年10月15日</td>\n      <td>15.50%</td>\n      <td>14.50%</td>\n      <td>-1.00%</td>\n      <td>13.50%</td>\n      <td>12.50%</td>\n      <td>-1.00%</td>\n      <td>-3.72%</td>\n      <td>-4.05%</td>\n    </tr>\n  </tbody>\n</table>\n</div>"},"execution_count":45}],"source":"Deposit.head()"},{"cell_type":"code","execution_count":46,"id":"committed-edward","metadata":{"jupyter":{},"tags":[],"slideshow":{"slide_type":"slide"},"id":"BB1943091E084AFC8BE6E02B2FF86CF2","trusted":true,"collapsed":false,"scrolled":false},"outputs":[{"output_type":"execute_result","metadata":{},"data":{"text/plain":"       Month  Business_Index Business_Index_YOY  Land_DevelopArea_Index  \\\n0  2010年12月份          101.79             -1.80%                   96.03   \n1  2010年11月份          103.20              0.41%                   96.42   \n2  2010年10月份          103.57              1.51%                   94.46   \n3  2010年09月份          103.52              2.41%                   94.10   \n4  2010年08月份          104.11              4.03%                   94.42   \n\n  Land_DevelopArea_Index_YOY  Price_Index Price_Index_YOY  \n0                      0.36%       104.54          -9.25%  \n1                      1.11%       104.34          -9.36%  \n2                     -0.14%       104.55          -8.99%  \n3                      0.18%       104.03          -9.05%  \n4                      1.25%       103.57          -8.96%  ","text/html":"<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>Month</th>\n      <th>Business_Index</th>\n      <th>Business_Index_YOY</th>\n      <th>Land_DevelopArea_Index</th>\n      <th>Land_DevelopArea_Index_YOY</th>\n      <th>Price_Index</th>\n      <th>Price_Index_YOY</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <td>0</td>\n      <td>2010年12月份</td>\n      <td>101.79</td>\n      <td>-1.80%</td>\n      <td>96.03</td>\n      <td>0.36%</td>\n      <td>104.54</td>\n      <td>-9.25%</td>\n    </tr>\n    <tr>\n      <td>1</td>\n      <td>2010年11月份</td>\n      <td>103.20</td>\n      <td>0.41%</td>\n      <td>96.42</td>\n      <td>1.11%</td>\n      <td>104.34</td>\n      <td>-9.36%</td>\n    </tr>\n    <tr>\n      <td>2</td>\n      <td>2010年10月份</td>\n      <td>103.57</td>\n      <td>1.51%</td>\n      <td>94.46</td>\n      <td>-0.14%</td>\n      <td>104.55</td>\n      <td>-8.99%</td>\n    </tr>\n    <tr>\n      <td>3</td>\n      <td>2010年09月份</td>\n      <td>103.52</td>\n      <td>2.41%</td>\n      <td>94.10</td>\n      <td>0.18%</td>\n      <td>104.03</td>\n      <td>-9.05%</td>\n    </tr>\n    <tr>\n      <td>4</td>\n      <td>2010年08月份</td>\n      <td>104.11</td>\n      <td>4.03%</td>\n      <td>94.42</td>\n      <td>1.25%</td>\n      <td>103.57</td>\n      <td>-8.96%</td>\n    </tr>\n  </tbody>\n</table>\n</div>"},"execution_count":46}],"source":"Housing_price.head()"},{"cell_type":"code","execution_count":47,"id":"asian-greene","metadata":{"jupyter":{},"tags":[],"slideshow":{"slide_type":"slide"},"id":"17EF45960EFD4B7B8A04374A78E15FC7","trusted":true,"collapsed":false,"scrolled":false},"outputs":[],"source":"cleardata(Housing_price)"},{"cell_type":"code","execution_count":48,"id":"permanent-panama","metadata":{"jupyter":{},"tags":[],"slideshow":{"slide_type":"slide"},"id":"157F9DF59F8743B5909298C7B467C710","trusted":true,"collapsed":false,"scrolled":false},"outputs":[{"output_type":"display_data","metadata":{"needs_background":"light"},"data":{"text/plain":"<Figure size 3000x4200 with 6 Axes>","text/html":"<img src=\"https://cdn.kesci.com/upload/rt/157F9DF59F8743B5909298C7B467C710/qtwt0a4n3p.png\">"}}],"source":"expolt_show(Housing_price)"},{"cell_type":"code","execution_count":49,"id":"specified-restaurant","metadata":{"jupyter":{},"tags":[],"slideshow":{"slide_type":"slide"},"id":"47B08BF5F9E84B058B5C8E915E21B59B","trusted":true,"collapsed":false,"scrolled":false},"outputs":[{"output_type":"execute_result","metadata":{},"data":{"text/plain":"       Month    YOY Cumulative_Growth\n0  2021年02月份  0.00%            35.10%\n1  2020年12月份  7.30%             2.80%\n2  2020年11月份  7.00%             2.30%\n3  2020年10月份  6.90%             1.80%\n4  2020年09月份  6.90%             1.20%","text/html":"<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>Month</th>\n      <th>YOY</th>\n      <th>Cumulative_Growth</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <td>0</td>\n      <td>2021年02月份</td>\n      <td>0.00%</td>\n      <td>35.10%</td>\n    </tr>\n    <tr>\n      <td>1</td>\n      <td>2020年12月份</td>\n      <td>7.30%</td>\n      <td>2.80%</td>\n    </tr>\n    <tr>\n      <td>2</td>\n      <td>2020年11月份</td>\n      <td>7.00%</td>\n      <td>2.30%</td>\n    </tr>\n    <tr>\n      <td>3</td>\n      <td>2020年10月份</td>\n      <td>6.90%</td>\n      <td>1.80%</td>\n    </tr>\n    <tr>\n      <td>4</td>\n      <td>2020年09月份</td>\n      <td>6.90%</td>\n      <td>1.20%</td>\n    </tr>\n  </tbody>\n</table>\n</div>"},"execution_count":49}],"source":"Industrial_add.head()"},{"cell_type":"code","execution_count":50,"id":"light-myanmar","metadata":{"jupyter":{},"tags":[],"slideshow":{"slide_type":"slide"},"id":"5F51CEA6B1464A989CA5851145D497E8","trusted":true,"collapsed":false,"scrolled":false},"outputs":[],"source":"cleardata(Industrial_add)"},{"cell_type":"code","execution_count":51,"id":"detected-sharp","metadata":{"jupyter":{},"tags":[],"slideshow":{"slide_type":"slide"},"id":"AE1EB5CE11F44BD6A5EBB6ABBD53BA90","trusted":true,"collapsed":false,"scrolled":false},"outputs":[],"source":"Industrial_add[\"Cumulative_Growth\"]=Industrial_add[\"Cumulative_Growth\"].apply(lambda x:float(x[:-1]))"},{"cell_type":"code","execution_count":52,"id":"inappropriate-modeling","metadata":{"jupyter":{},"tags":[],"slideshow":{"slide_type":"slide"},"id":"5ECAAB5CCED64F4A8635D325E33E9B3B","trusted":true,"collapsed":false,"scrolled":false},"outputs":[{"output_type":"display_data","metadata":{"needs_background":"light"},"data":{"text/plain":"<Figure size 3000x1800 with 2 Axes>","text/html":"<img src=\"https://cdn.kesci.com/upload/rt/5ECAAB5CCED64F4A8635D325E33E9B3B/qtwt0htaa0.png\">"}}],"source":"expolt_show(Industrial_add)"},{"cell_type":"code","execution_count":53,"id":"collaborative-blast","metadata":{"jupyter":{},"tags":[],"slideshow":{"slide_type":"slide"},"id":"8B3A7FF36690470D9B2CD44C595999DF","trusted":true,"collapsed":false,"scrolled":false},"outputs":[{"output_type":"execute_result","metadata":{},"data":{"text/plain":"       Month Current_Export Current_Export_YOY Current_Export_Comparative  \\\n0  2021年02月份              -                  -                          -   \n1  2020年12月份           2819             18.10%                      5.17%   \n2  2020年11月份           2681             21.10%                     13.02%   \n3  2020年10月份           2372             11.40%                     -1.07%   \n4  2020年09月份           2398              9.90%                      1.91%   \n\n  Current_Import Current_Import_YOY Current_Import_Comparative  Total_Export  \\\n0              -                  -                          -        4689.0   \n1           2038              6.50%                      5.76%       25906.0   \n2           1926              4.50%                      7.78%       23167.0   \n3           1787              4.70%                    -11.85%       20486.0   \n4           2028             13.20%                     14.99%       18114.0   \n\n  Total_Export_YOY  Total_Import Total_Import_YOY  \n0           60.60%        3656.0           22.20%  \n1            3.60%       20556.0           -1.10%  \n2            2.50%       18567.0           -1.60%  \n3            0.50%       16641.0           -2.30%  \n4           -0.80%       14853.0           -3.10%  ","text/html":"<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>Month</th>\n      <th>Current_Export</th>\n      <th>Current_Export_YOY</th>\n      <th>Current_Export_Comparative</th>\n      <th>Current_Import</th>\n      <th>Current_Import_YOY</th>\n      <th>Current_Import_Comparative</th>\n      <th>Total_Export</th>\n      <th>Total_Export_YOY</th>\n      <th>Total_Import</th>\n      <th>Total_Import_YOY</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <td>0</td>\n      <td>2021年02月份</td>\n      <td>-</td>\n      <td>-</td>\n      <td>-</td>\n      <td>-</td>\n      <td>-</td>\n      <td>-</td>\n      <td>4689.0</td>\n      <td>60.60%</td>\n      <td>3656.0</td>\n      <td>22.20%</td>\n    </tr>\n    <tr>\n      <td>1</td>\n      <td>2020年12月份</td>\n      <td>2819</td>\n      <td>18.10%</td>\n      <td>5.17%</td>\n      <td>2038</td>\n      <td>6.50%</td>\n      <td>5.76%</td>\n      <td>25906.0</td>\n      <td>3.60%</td>\n      <td>20556.0</td>\n      <td>-1.10%</td>\n    </tr>\n    <tr>\n      <td>2</td>\n      <td>2020年11月份</td>\n      <td>2681</td>\n      <td>21.10%</td>\n      <td>13.02%</td>\n      <td>1926</td>\n      <td>4.50%</td>\n      <td>7.78%</td>\n      <td>23167.0</td>\n      <td>2.50%</td>\n      <td>18567.0</td>\n      <td>-1.60%</td>\n    </tr>\n    <tr>\n      <td>3</td>\n      <td>2020年10月份</td>\n      <td>2372</td>\n      <td>11.40%</td>\n      <td>-1.07%</td>\n      <td>1787</td>\n      <td>4.70%</td>\n      <td>-11.85%</td>\n      <td>20486.0</td>\n      <td>0.50%</td>\n      <td>16641.0</td>\n      <td>-2.30%</td>\n    </tr>\n    <tr>\n      <td>4</td>\n      <td>2020年09月份</td>\n      <td>2398</td>\n      <td>9.90%</td>\n      <td>1.91%</td>\n      <td>2028</td>\n      <td>13.20%</td>\n      <td>14.99%</td>\n      <td>18114.0</td>\n      <td>-0.80%</td>\n      <td>14853.0</td>\n      <td>-3.10%</td>\n    </tr>\n  </tbody>\n</table>\n</div>"},"execution_count":53}],"source":"Import_export.head()"},{"cell_type":"code","execution_count":54,"id":"clean-saint","metadata":{"jupyter":{},"tags":[],"slideshow":{"slide_type":"slide"},"id":"C615B07BDDE44A6183AF9F3682167DF7","trusted":true,"collapsed":false,"scrolled":false},"outputs":[],"source":"cleardata(Import_export)"},{"cell_type":"code","execution_count":55,"id":"united-mother","metadata":{"jupyter":{},"tags":[],"slideshow":{"slide_type":"slide"},"id":"89E5D05F20E143B686F134A70D26B519","trusted":true,"collapsed":false,"scrolled":false},"outputs":[{"output_type":"execute_result","metadata":{},"data":{"text/plain":"       Month Current_Export  Current_Export_YOY Current_Export_Comparative  \\\n0 2021-02-01              -                 NaN                          -   \n1 2020-12-01           2819               0.181                      5.17%   \n2 2020-11-01           2681               0.211                     13.02%   \n3 2020-10-01           2372               0.114                     -1.07%   \n4 2020-09-01           2398               0.099                      1.91%   \n\n  Current_Import  Current_Import_YOY Current_Import_Comparative  Total_Export  \\\n0              -                 NaN                          -        4689.0   \n1           2038               0.065                      5.76%       25906.0   \n2           1926               0.045                      7.78%       23167.0   \n3           1787               0.047                    -11.85%       20486.0   \n4           2028               0.132                     14.99%       18114.0   \n\n   Total_Export_YOY  Total_Import  Total_Import_YOY  \n0             0.606        3656.0             0.222  \n1             0.036       20556.0            -0.011  \n2             0.025       18567.0            -0.016  \n3             0.005       16641.0            -0.023  \n4            -0.008       14853.0            -0.031  ","text/html":"<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>Month</th>\n      <th>Current_Export</th>\n      <th>Current_Export_YOY</th>\n      <th>Current_Export_Comparative</th>\n      <th>Current_Import</th>\n      <th>Current_Import_YOY</th>\n      <th>Current_Import_Comparative</th>\n      <th>Total_Export</th>\n      <th>Total_Export_YOY</th>\n      <th>Total_Import</th>\n      <th>Total_Import_YOY</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <td>0</td>\n      <td>2021-02-01</td>\n      <td>-</td>\n      <td>NaN</td>\n      <td>-</td>\n      <td>-</td>\n      <td>NaN</td>\n      <td>-</td>\n      <td>4689.0</td>\n      <td>0.606</td>\n      <td>3656.0</td>\n      <td>0.222</td>\n    </tr>\n    <tr>\n      <td>1</td>\n      <td>2020-12-01</td>\n      <td>2819</td>\n      <td>0.181</td>\n      <td>5.17%</td>\n      <td>2038</td>\n      <td>0.065</td>\n      <td>5.76%</td>\n      <td>25906.0</td>\n      <td>0.036</td>\n      <td>20556.0</td>\n      <td>-0.011</td>\n    </tr>\n    <tr>\n      <td>2</td>\n      <td>2020-11-01</td>\n      <td>2681</td>\n      <td>0.211</td>\n      <td>13.02%</td>\n      <td>1926</td>\n      <td>0.045</td>\n      <td>7.78%</td>\n      <td>23167.0</td>\n      <td>0.025</td>\n      <td>18567.0</td>\n      <td>-0.016</td>\n    </tr>\n    <tr>\n      <td>3</td>\n      <td>2020-10-01</td>\n      <td>2372</td>\n      <td>0.114</td>\n      <td>-1.07%</td>\n      <td>1787</td>\n      <td>0.047</td>\n      <td>-11.85%</td>\n      <td>20486.0</td>\n      <td>0.005</td>\n      <td>16641.0</td>\n      <td>-0.023</td>\n    </tr>\n    <tr>\n      <td>4</td>\n      <td>2020-09-01</td>\n      <td>2398</td>\n      <td>0.099</td>\n      <td>1.91%</td>\n      <td>2028</td>\n      <td>0.132</td>\n      <td>14.99%</td>\n      <td>18114.0</td>\n      <td>-0.008</td>\n      <td>14853.0</td>\n      <td>-0.031</td>\n    </tr>\n  </tbody>\n</table>\n</div>"},"execution_count":55}],"source":"Import_export.head()"},{"cell_type":"code","execution_count":56,"id":"forty-dialogue","metadata":{"jupyter":{},"tags":[],"slideshow":{"slide_type":"slide"},"id":"01B84E6622F2487E8EC817B5197E5618","trusted":true,"collapsed":false,"scrolled":false},"outputs":[{"output_type":"execute_result","metadata":{},"data":{"text/plain":"       Month         M2  M2_YOY M2_Comparative        M1  M1_YOY  \\\n0  2021年02月份  2236000.0  10.10%          1.04%  593500.0   7.40%   \n1  2021年01月份  2213000.0   9.40%          1.20%  625600.0  14.70%   \n2  2020年12月份  2186800.0  10.10%          0.68%  625600.0   8.60%   \n3  2020年11月份  2172000.0  10.70%          1.04%  618600.0  10.00%   \n4  2020年10月份  2149700.0  10.50%         -0.67%  609200.0   9.10%   \n\n  M1_Comparative       M0  M0_YOY M0_Comparative  \n0         -5.13%  91900.0   4.20%          2.57%  \n1          0.00%  89600.0  -3.90%          6.29%  \n2          1.13%  84300.0   9.20%          3.31%  \n3          1.54%  81600.0  10.30%          0.74%  \n4          1.15%  81000.0  10.40%         -1.70%  ","text/html":"<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>Month</th>\n      <th>M2</th>\n      <th>M2_YOY</th>\n      <th>M2_Comparative</th>\n      <th>M1</th>\n      <th>M1_YOY</th>\n      <th>M1_Comparative</th>\n      <th>M0</th>\n      <th>M0_YOY</th>\n      <th>M0_Comparative</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <td>0</td>\n      <td>2021年02月份</td>\n      <td>2236000.0</td>\n      <td>10.10%</td>\n      <td>1.04%</td>\n      <td>593500.0</td>\n      <td>7.40%</td>\n      <td>-5.13%</td>\n      <td>91900.0</td>\n      <td>4.20%</td>\n      <td>2.57%</td>\n    </tr>\n    <tr>\n      <td>1</td>\n      <td>2021年01月份</td>\n      <td>2213000.0</td>\n      <td>9.40%</td>\n      <td>1.20%</td>\n      <td>625600.0</td>\n      <td>14.70%</td>\n      <td>0.00%</td>\n      <td>89600.0</td>\n      <td>-3.90%</td>\n      <td>6.29%</td>\n    </tr>\n    <tr>\n      <td>2</td>\n      <td>2020年12月份</td>\n      <td>2186800.0</td>\n      <td>10.10%</td>\n      <td>0.68%</td>\n      <td>625600.0</td>\n      <td>8.60%</td>\n      <td>1.13%</td>\n      <td>84300.0</td>\n      <td>9.20%</td>\n      <td>3.31%</td>\n    </tr>\n    <tr>\n      <td>3</td>\n      <td>2020年11月份</td>\n      <td>2172000.0</td>\n      <td>10.70%</td>\n      <td>1.04%</td>\n      <td>618600.0</td>\n      <td>10.00%</td>\n      <td>1.54%</td>\n      <td>81600.0</td>\n      <td>10.30%</td>\n      <td>0.74%</td>\n    </tr>\n    <tr>\n      <td>4</td>\n      <td>2020年10月份</td>\n      <td>2149700.0</td>\n      <td>10.50%</td>\n      <td>-0.67%</td>\n      <td>609200.0</td>\n      <td>9.10%</td>\n      <td>1.15%</td>\n      <td>81000.0</td>\n      <td>10.40%</td>\n      <td>-1.70%</td>\n    </tr>\n  </tbody>\n</table>\n</div>"},"execution_count":56}],"source":"Money_supply.head()"},{"cell_type":"code","execution_count":57,"id":"varied-filter","metadata":{"jupyter":{},"tags":[],"slideshow":{"slide_type":"slide"},"id":"4F6299F32125413898622ED4E31F4223","trusted":true,"collapsed":false,"scrolled":false},"outputs":[],"source":"cleardata(Money_supply)"},{"cell_type":"code","execution_count":58,"id":"atlantic-revelation","metadata":{"jupyter":{},"tags":[],"slideshow":{"slide_type":"slide"},"id":"4639019A96914ED98BE1A62D40F4DE68","trusted":true,"collapsed":false,"scrolled":false},"outputs":[{"output_type":"execute_result","metadata":{},"data":{"text/plain":"       Month         M2  M2_YOY M2_Comparative        M1  M1_YOY  \\\n0 2021-02-01  2236000.0   0.101          1.04%  593500.0   0.074   \n1 2021-01-01  2213000.0   0.094          1.20%  625600.0   0.147   \n2 2020-12-01  2186800.0   0.101          0.68%  625600.0   0.086   \n3 2020-11-01  2172000.0   0.107          1.04%  618600.0   0.100   \n4 2020-10-01  2149700.0   0.105         -0.67%  609200.0   0.091   \n\n  M1_Comparative       M0  M0_YOY M0_Comparative  \n0         -5.13%  91900.0   0.042          2.57%  \n1          0.00%  89600.0  -0.039          6.29%  \n2          1.13%  84300.0   0.092          3.31%  \n3          1.54%  81600.0   0.103          0.74%  \n4          1.15%  81000.0   0.104         -1.70%  ","text/html":"<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>Month</th>\n      <th>M2</th>\n      <th>M2_YOY</th>\n      <th>M2_Comparative</th>\n      <th>M1</th>\n      <th>M1_YOY</th>\n      <th>M1_Comparative</th>\n      <th>M0</th>\n      <th>M0_YOY</th>\n      <th>M0_Comparative</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <td>0</td>\n      <td>2021-02-01</td>\n      <td>2236000.0</td>\n      <td>0.101</td>\n      <td>1.04%</td>\n      <td>593500.0</td>\n      <td>0.074</td>\n      <td>-5.13%</td>\n      <td>91900.0</td>\n      <td>0.042</td>\n      <td>2.57%</td>\n    </tr>\n    <tr>\n      <td>1</td>\n      <td>2021-01-01</td>\n      <td>2213000.0</td>\n      <td>0.094</td>\n      <td>1.20%</td>\n      <td>625600.0</td>\n      <td>0.147</td>\n      <td>0.00%</td>\n      <td>89600.0</td>\n      <td>-0.039</td>\n      <td>6.29%</td>\n    </tr>\n    <tr>\n      <td>2</td>\n      <td>2020-12-01</td>\n      <td>2186800.0</td>\n      <td>0.101</td>\n      <td>0.68%</td>\n      <td>625600.0</td>\n      <td>0.086</td>\n      <td>1.13%</td>\n      <td>84300.0</td>\n      <td>0.092</td>\n      <td>3.31%</td>\n    </tr>\n    <tr>\n      <td>3</td>\n      <td>2020-11-01</td>\n      <td>2172000.0</td>\n      <td>0.107</td>\n      <td>1.04%</td>\n      <td>618600.0</td>\n      <td>0.100</td>\n      <td>1.54%</td>\n      <td>81600.0</td>\n      <td>0.103</td>\n      <td>0.74%</td>\n    </tr>\n    <tr>\n      <td>4</td>\n      <td>2020-10-01</td>\n      <td>2149700.0</td>\n      <td>0.105</td>\n      <td>-0.67%</td>\n      <td>609200.0</td>\n      <td>0.091</td>\n      <td>1.15%</td>\n      <td>81000.0</td>\n      <td>0.104</td>\n      <td>-1.70%</td>\n    </tr>\n  </tbody>\n</table>\n</div>"},"execution_count":58}],"source":"Money_supply.head()"},{"cell_type":"code","execution_count":59,"id":"associate-department","metadata":{"jupyter":{},"tags":[],"slideshow":{"slide_type":"slide"},"id":"C4F03FC88D96420E9819B01AFFBC3490","trusted":true,"collapsed":false,"scrolled":false},"outputs":[{"output_type":"execute_result","metadata":{},"data":{"text/plain":"   Start_Date    End_Date  Transaction_End  Transaction_DailyAvg  \\\n0  2017年6月12日  2017年6月16日            12205                 12541   \n1   2017年6月5日   2017年6月9日            12619                 12850   \n2  2017年5月31日   2017年6月2日            12069                 12175   \n3  2017年5月22日  2017年5月26日            12228                 12815   \n4  2017年5月15日  2017年5月19日            12333                 12638   \n\n   Bank_Transfer_Added  Bank_Transfer_Deducted  Bank_Transfer_Net  \\\n0                 3985                    4662               -677   \n1                 4075                    4484               -409   \n2                 3058                    2813                245   \n3                 4498                    5393               -895   \n4                 4400                    4415                -15   \n\n  Shanghai_Index_Closing Shanghai_Index_Change  \n0                3123.17                -1.12%  \n1                 3158.4                 1.70%  \n2                3105.54                -0.15%  \n3                3110.06                 0.63%  \n4                3090.63                 0.23%  ","text/html":"<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>Start_Date</th>\n      <th>End_Date</th>\n      <th>Transaction_End</th>\n      <th>Transaction_DailyAvg</th>\n      <th>Bank_Transfer_Added</th>\n      <th>Bank_Transfer_Deducted</th>\n      <th>Bank_Transfer_Net</th>\n      <th>Shanghai_Index_Closing</th>\n      <th>Shanghai_Index_Change</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <td>0</td>\n      <td>2017年6月12日</td>\n      <td>2017年6月16日</td>\n      <td>12205</td>\n      <td>12541</td>\n      <td>3985</td>\n      <td>4662</td>\n      <td>-677</td>\n      <td>3123.17</td>\n      <td>-1.12%</td>\n    </tr>\n    <tr>\n      <td>1</td>\n      <td>2017年6月5日</td>\n      <td>2017年6月9日</td>\n      <td>12619</td>\n      <td>12850</td>\n      <td>4075</td>\n      <td>4484</td>\n      <td>-409</td>\n      <td>3158.4</td>\n      <td>1.70%</td>\n    </tr>\n    <tr>\n      <td>2</td>\n      <td>2017年5月31日</td>\n      <td>2017年6月2日</td>\n      <td>12069</td>\n      <td>12175</td>\n      <td>3058</td>\n      <td>2813</td>\n      <td>245</td>\n      <td>3105.54</td>\n      <td>-0.15%</td>\n    </tr>\n    <tr>\n      <td>3</td>\n      <td>2017年5月22日</td>\n      <td>2017年5月26日</td>\n      <td>12228</td>\n      <td>12815</td>\n      <td>4498</td>\n      <td>5393</td>\n      <td>-895</td>\n      <td>3110.06</td>\n      <td>0.63%</td>\n    </tr>\n    <tr>\n      <td>4</td>\n      <td>2017年5月15日</td>\n      <td>2017年5月19日</td>\n      <td>12333</td>\n      <td>12638</td>\n      <td>4400</td>\n      <td>4415</td>\n      <td>-15</td>\n      <td>3090.63</td>\n      <td>0.23%</td>\n    </tr>\n  </tbody>\n</table>\n</div>"},"execution_count":59}],"source":"Bank_Transfer.head()"},{"cell_type":"code","execution_count":60,"id":"grave-poultry","metadata":{"jupyter":{},"tags":[],"slideshow":{"slide_type":"slide"},"id":"BCC5F282661B4E4D80DD73261BF26A0F","trusted":true,"collapsed":false,"scrolled":false},"outputs":[{"output_type":"execute_result","metadata":{},"data":{"text/plain":"     Quarter Climate_Index_Enterprise Climate_Index_Enterprise_YOY  \\\n0  2020年第4季度                   121.91                       21.91%   \n1  2020年第3季度                   121.15                       21.15%   \n2  2020年第2季度                   109.12                        9.12%   \n3  2020年第1季度                    88.22                      -11.78%   \n4  2019年第4季度                    122.8                       22.80%   \n\n  Climate_Index_Enterprise_Comparative  Macro_econ_Climate_Index  \\\n0                                0.76%                    123.44   \n1                               12.04%                    122.54   \n2                               20.90%                    110.41   \n3                              -34.58%                     90.86   \n4                               -0.60%                    123.60   \n\n  Macro_econ_Climate_Index_YOY Macro_econ_Climate_Index_Comparative  \n0                       23.44%                                0.90%  \n1                       22.54%                               12.13%  \n2                       10.41%                               19.55%  \n3                       -9.14%                              -32.74%  \n4                       23.60%                               -0.70%  ","text/html":"<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>Quarter</th>\n      <th>Climate_Index_Enterprise</th>\n      <th>Climate_Index_Enterprise_YOY</th>\n      <th>Climate_Index_Enterprise_Comparative</th>\n      <th>Macro_econ_Climate_Index</th>\n      <th>Macro_econ_Climate_Index_YOY</th>\n      <th>Macro_econ_Climate_Index_Comparative</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <td>0</td>\n      <td>2020年第4季度</td>\n      <td>121.91</td>\n      <td>21.91%</td>\n      <td>0.76%</td>\n      <td>123.44</td>\n      <td>23.44%</td>\n      <td>0.90%</td>\n    </tr>\n    <tr>\n      <td>1</td>\n      <td>2020年第3季度</td>\n      <td>121.15</td>\n      <td>21.15%</td>\n      <td>12.04%</td>\n      <td>122.54</td>\n      <td>22.54%</td>\n      <td>12.13%</td>\n    </tr>\n    <tr>\n      <td>2</td>\n      <td>2020年第2季度</td>\n      <td>109.12</td>\n      <td>9.12%</td>\n      <td>20.90%</td>\n      <td>110.41</td>\n      <td>10.41%</td>\n      <td>19.55%</td>\n    </tr>\n    <tr>\n      <td>3</td>\n      <td>2020年第1季度</td>\n      <td>88.22</td>\n      <td>-11.78%</td>\n      <td>-34.58%</td>\n      <td>90.86</td>\n      <td>-9.14%</td>\n      <td>-32.74%</td>\n    </tr>\n    <tr>\n      <td>4</td>\n      <td>2019年第4季度</td>\n      <td>122.8</td>\n      <td>22.80%</td>\n      <td>-0.60%</td>\n      <td>123.60</td>\n      <td>23.60%</td>\n      <td>-0.70%</td>\n    </tr>\n  </tbody>\n</table>\n</div>"},"execution_count":60}],"source":"Enterprise_Confidence.head()"},{"cell_type":"code","execution_count":61,"id":"official-lambda","metadata":{"jupyter":{},"tags":[],"slideshow":{"slide_type":"slide"},"id":"F0A4BAC81B5843698762EEC9E11E41FA","trusted":true,"collapsed":false,"scrolled":false},"outputs":[{"output_type":"execute_result","metadata":{},"data":{"text/plain":"       Month   CPGI CPGI_YOY CPGI_Comparative  Agricultural_Products_Index  \\\n0  2021年01月份  101.2    0.20%            1.00%                        101.8   \n1  2020年12月份  100.2   -0.30%            1.21%                        100.6   \n2  2020年11月份   99.0   -0.40%            0.51%                        100.0   \n3  2020年10月份   98.5   -0.40%           -0.20%                        102.8   \n4  2020年09月份   98.7   -0.20%           -0.20%                        106.1   \n\n   API_YOY API_Comparative  Mineral_Products_Index MPI_YOY MPI_Comparative  \\\n0  -14.17%           1.19%                   110.7   3.55%           1.84%   \n1  -14.09%           0.60%                   108.7   1.68%           2.16%   \n2  -13.72%          -2.72%                   106.4   0.57%           1.04%   \n3   -8.38%          -3.11%                   105.3  -1.96%           1.54%   \n4   -2.93%          -1.67%                   103.7  -4.69%           0.19%   \n\n   Kerosene_Power_Index KPI_YOY KPI_Comparative  \n0                  93.7  -6.67%           1.19%  \n1                  92.6  -5.51%           2.21%  \n2                  90.6  -3.72%           0.44%  \n3                  90.2  -2.80%          -0.99%  \n4                  91.1  -2.57%           0.00%  ","text/html":"<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>Month</th>\n      <th>CPGI</th>\n      <th>CPGI_YOY</th>\n      <th>CPGI_Comparative</th>\n      <th>Agricultural_Products_Index</th>\n      <th>API_YOY</th>\n      <th>API_Comparative</th>\n      <th>Mineral_Products_Index</th>\n      <th>MPI_YOY</th>\n      <th>MPI_Comparative</th>\n      <th>Kerosene_Power_Index</th>\n      <th>KPI_YOY</th>\n      <th>KPI_Comparative</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <td>0</td>\n      <td>2021年01月份</td>\n      <td>101.2</td>\n      <td>0.20%</td>\n      <td>1.00%</td>\n      <td>101.8</td>\n      <td>-14.17%</td>\n      <td>1.19%</td>\n      <td>110.7</td>\n      <td>3.55%</td>\n      <td>1.84%</td>\n      <td>93.7</td>\n      <td>-6.67%</td>\n      <td>1.19%</td>\n    </tr>\n    <tr>\n      <td>1</td>\n      <td>2020年12月份</td>\n      <td>100.2</td>\n      <td>-0.30%</td>\n      <td>1.21%</td>\n      <td>100.6</td>\n      <td>-14.09%</td>\n      <td>0.60%</td>\n      <td>108.7</td>\n      <td>1.68%</td>\n      <td>2.16%</td>\n      <td>92.6</td>\n      <td>-5.51%</td>\n      <td>2.21%</td>\n    </tr>\n    <tr>\n      <td>2</td>\n      <td>2020年11月份</td>\n      <td>99.0</td>\n      <td>-0.40%</td>\n      <td>0.51%</td>\n      <td>100.0</td>\n      <td>-13.72%</td>\n      <td>-2.72%</td>\n      <td>106.4</td>\n      <td>0.57%</td>\n      <td>1.04%</td>\n      <td>90.6</td>\n      <td>-3.72%</td>\n      <td>0.44%</td>\n    </tr>\n    <tr>\n      <td>3</td>\n      <td>2020年10月份</td>\n      <td>98.5</td>\n      <td>-0.40%</td>\n      <td>-0.20%</td>\n      <td>102.8</td>\n      <td>-8.38%</td>\n      <td>-3.11%</td>\n      <td>105.3</td>\n      <td>-1.96%</td>\n      <td>1.54%</td>\n      <td>90.2</td>\n      <td>-2.80%</td>\n      <td>-0.99%</td>\n    </tr>\n    <tr>\n      <td>4</td>\n      <td>2020年09月份</td>\n      <td>98.7</td>\n      <td>-0.20%</td>\n      <td>-0.20%</td>\n      <td>106.1</td>\n      <td>-2.93%</td>\n      <td>-1.67%</td>\n      <td>103.7</td>\n      <td>-4.69%</td>\n      <td>0.19%</td>\n      <td>91.1</td>\n      <td>-2.57%</td>\n      <td>0.00%</td>\n    </tr>\n  </tbody>\n</table>\n</div>"},"execution_count":61}],"source":"CPGI.head()"},{"cell_type":"code","execution_count":62,"id":"aquatic-tender","metadata":{"jupyter":{},"tags":[],"slideshow":{"slide_type":"slide"},"id":"8F46AA012AD14BF49B574891C23A3382","trusted":true,"collapsed":false,"scrolled":false},"outputs":[],"source":"cleardata(CPGI)\ncleardata(RSCG)"},{"cell_type":"code","execution_count":63,"id":"chief-builder","metadata":{"jupyter":{},"tags":[],"slideshow":{"slide_type":"slide"},"id":"2AA088E219A344C09416F7E6CDB8E231","trusted":true,"collapsed":false,"scrolled":false},"outputs":[{"output_type":"execute_result","metadata":{},"data":{"text/plain":"       Month   CPGI  CPGI_YOY CPGI_Comparative  Agricultural_Products_Index  \\\n0 2021-01-01  101.2     0.002            1.00%                        101.8   \n1 2020-12-01  100.2    -0.003            1.21%                        100.6   \n2 2020-11-01   99.0    -0.004            0.51%                        100.0   \n3 2020-10-01   98.5    -0.004           -0.20%                        102.8   \n4 2020-09-01   98.7    -0.002           -0.20%                        106.1   \n\n   API_YOY API_Comparative  Mineral_Products_Index  MPI_YOY MPI_Comparative  \\\n0  -0.1417           1.19%                   110.7   0.0355           1.84%   \n1  -0.1409           0.60%                   108.7   0.0168           2.16%   \n2  -0.1372          -2.72%                   106.4   0.0057           1.04%   \n3  -0.0838          -3.11%                   105.3  -0.0196           1.54%   \n4  -0.0293          -1.67%                   103.7  -0.0469           0.19%   \n\n   Kerosene_Power_Index  KPI_YOY KPI_Comparative  \n0                  93.7  -0.0667           1.19%  \n1                  92.6  -0.0551           2.21%  \n2                  90.6  -0.0372           0.44%  \n3                  90.2  -0.0280          -0.99%  \n4                  91.1  -0.0257           0.00%  ","text/html":"<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>Month</th>\n      <th>CPGI</th>\n      <th>CPGI_YOY</th>\n      <th>CPGI_Comparative</th>\n      <th>Agricultural_Products_Index</th>\n      <th>API_YOY</th>\n      <th>API_Comparative</th>\n      <th>Mineral_Products_Index</th>\n      <th>MPI_YOY</th>\n      <th>MPI_Comparative</th>\n      <th>Kerosene_Power_Index</th>\n      <th>KPI_YOY</th>\n      <th>KPI_Comparative</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <td>0</td>\n      <td>2021-01-01</td>\n      <td>101.2</td>\n      <td>0.002</td>\n      <td>1.00%</td>\n      <td>101.8</td>\n      <td>-0.1417</td>\n      <td>1.19%</td>\n      <td>110.7</td>\n      <td>0.0355</td>\n      <td>1.84%</td>\n      <td>93.7</td>\n      <td>-0.0667</td>\n      <td>1.19%</td>\n    </tr>\n    <tr>\n      <td>1</td>\n      <td>2020-12-01</td>\n      <td>100.2</td>\n      <td>-0.003</td>\n      <td>1.21%</td>\n      <td>100.6</td>\n      <td>-0.1409</td>\n      <td>0.60%</td>\n      <td>108.7</td>\n      <td>0.0168</td>\n      <td>2.16%</td>\n      <td>92.6</td>\n      <td>-0.0551</td>\n      <td>2.21%</td>\n    </tr>\n    <tr>\n      <td>2</td>\n      <td>2020-11-01</td>\n      <td>99.0</td>\n      <td>-0.004</td>\n      <td>0.51%</td>\n      <td>100.0</td>\n      <td>-0.1372</td>\n      <td>-2.72%</td>\n      <td>106.4</td>\n      <td>0.0057</td>\n      <td>1.04%</td>\n      <td>90.6</td>\n      <td>-0.0372</td>\n      <td>0.44%</td>\n    </tr>\n    <tr>\n      <td>3</td>\n      <td>2020-10-01</td>\n      <td>98.5</td>\n      <td>-0.004</td>\n      <td>-0.20%</td>\n      <td>102.8</td>\n      <td>-0.0838</td>\n      <td>-3.11%</td>\n      <td>105.3</td>\n      <td>-0.0196</td>\n      <td>1.54%</td>\n      <td>90.2</td>\n      <td>-0.0280</td>\n      <td>-0.99%</td>\n    </tr>\n    <tr>\n      <td>4</td>\n      <td>2020-09-01</td>\n      <td>98.7</td>\n      <td>-0.002</td>\n      <td>-0.20%</td>\n      <td>106.1</td>\n      <td>-0.0293</td>\n      <td>-1.67%</td>\n      <td>103.7</td>\n      <td>-0.0469</td>\n      <td>0.19%</td>\n      <td>91.1</td>\n      <td>-0.0257</td>\n      <td>0.00%</td>\n    </tr>\n  </tbody>\n</table>\n</div>"},"execution_count":63}],"source":"CPGI.head()"},{"cell_type":"code","execution_count":64,"id":"convenient-excess","metadata":{"jupyter":{},"tags":[],"slideshow":{"slide_type":"slide"},"id":"38ECF6881AFE4074891A8572F8CECF74","trusted":true,"collapsed":false,"scrolled":false},"outputs":[{"output_type":"execute_result","metadata":{},"data":{"text/plain":"       Month    CCI CCI_YOY CCI_Comparative    CSI CSI_YOY CSI_Comparative  \\\n0  2021年01月份  122.8  -2.85%           0.57%  118.0  -1.67%           0.60%   \n1  2020年12月份  122.1  -3.55%          -1.53%  117.3  -2.82%          -1.92%   \n2  2020年11月份  124.0  -0.48%           1.89%  119.6   1.36%           2.57%   \n3  2020年10月份  121.7  -2.09%           1.00%  116.6  -1.02%           1.39%   \n4  2020年09月份  120.5  -2.90%           3.52%  115.0  -2.71%           3.79%   \n\n     CEI CEI_YOY CEI_Comparative  \n0  126.0  -3.60%           0.64%  \n1  125.2  -4.13%          -1.42%  \n2  127.0  -1.47%           1.44%  \n3  125.2  -2.57%           0.81%  \n4  124.2  -2.97%           3.41%  ","text/html":"<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>Month</th>\n      <th>CCI</th>\n      <th>CCI_YOY</th>\n      <th>CCI_Comparative</th>\n      <th>CSI</th>\n      <th>CSI_YOY</th>\n      <th>CSI_Comparative</th>\n      <th>CEI</th>\n      <th>CEI_YOY</th>\n      <th>CEI_Comparative</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <td>0</td>\n      <td>2021年01月份</td>\n      <td>122.8</td>\n      <td>-2.85%</td>\n      <td>0.57%</td>\n      <td>118.0</td>\n      <td>-1.67%</td>\n      <td>0.60%</td>\n      <td>126.0</td>\n      <td>-3.60%</td>\n      <td>0.64%</td>\n    </tr>\n    <tr>\n      <td>1</td>\n      <td>2020年12月份</td>\n      <td>122.1</td>\n      <td>-3.55%</td>\n      <td>-1.53%</td>\n      <td>117.3</td>\n      <td>-2.82%</td>\n      <td>-1.92%</td>\n      <td>125.2</td>\n      <td>-4.13%</td>\n      <td>-1.42%</td>\n    </tr>\n    <tr>\n      <td>2</td>\n      <td>2020年11月份</td>\n      <td>124.0</td>\n      <td>-0.48%</td>\n      <td>1.89%</td>\n      <td>119.6</td>\n      <td>1.36%</td>\n      <td>2.57%</td>\n      <td>127.0</td>\n      <td>-1.47%</td>\n      <td>1.44%</td>\n    </tr>\n    <tr>\n      <td>3</td>\n      <td>2020年10月份</td>\n      <td>121.7</td>\n      <td>-2.09%</td>\n      <td>1.00%</td>\n      <td>116.6</td>\n      <td>-1.02%</td>\n      <td>1.39%</td>\n      <td>125.2</td>\n      <td>-2.57%</td>\n      <td>0.81%</td>\n    </tr>\n    <tr>\n      <td>4</td>\n      <td>2020年09月份</td>\n      <td>120.5</td>\n      <td>-2.90%</td>\n      <td>3.52%</td>\n      <td>115.0</td>\n      <td>-2.71%</td>\n      <td>3.79%</td>\n      <td>124.2</td>\n      <td>-2.97%</td>\n      <td>3.41%</td>\n    </tr>\n  </tbody>\n</table>\n</div>"},"execution_count":64}],"source":"CCI.head()"},{"cell_type":"code","execution_count":65,"id":"featured-buffalo","metadata":{"jupyter":{},"tags":[],"slideshow":{"slide_type":"slide"},"id":"886DB6E061D947478994DA8CC30FCC6C","trusted":true,"collapsed":false,"scrolled":false},"outputs":[],"source":"cleardata(CCI)"},{"cell_type":"code","execution_count":66,"id":"thirty-satisfaction","metadata":{"jupyter":{},"tags":[],"slideshow":{"slide_type":"slide"},"id":"DBF5879E012D4B0184ECC678EAAD08F1","trusted":true,"collapsed":false,"scrolled":false},"outputs":[{"output_type":"execute_result","metadata":{},"data":{"text/plain":"       Month    CCI  CCI_YOY CCI_Comparative    CSI  CSI_YOY CSI_Comparative  \\\n0 2021-01-01  122.8  -0.0285           0.57%  118.0  -0.0167           0.60%   \n1 2020-12-01  122.1  -0.0355          -1.53%  117.3  -0.0282          -1.92%   \n2 2020-11-01  124.0  -0.0048           1.89%  119.6   0.0136           2.57%   \n3 2020-10-01  121.7  -0.0209           1.00%  116.6  -0.0102           1.39%   \n4 2020-09-01  120.5  -0.0290           3.52%  115.0  -0.0271           3.79%   \n\n     CEI  CEI_YOY CEI_Comparative  \n0  126.0  -0.0360           0.64%  \n1  125.2  -0.0413          -1.42%  \n2  127.0  -0.0147           1.44%  \n3  125.2  -0.0257           0.81%  \n4  124.2  -0.0297           3.41%  ","text/html":"<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>Month</th>\n      <th>CCI</th>\n      <th>CCI_YOY</th>\n      <th>CCI_Comparative</th>\n      <th>CSI</th>\n      <th>CSI_YOY</th>\n      <th>CSI_Comparative</th>\n      <th>CEI</th>\n      <th>CEI_YOY</th>\n      <th>CEI_Comparative</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <td>0</td>\n      <td>2021-01-01</td>\n      <td>122.8</td>\n      <td>-0.0285</td>\n      <td>0.57%</td>\n      <td>118.0</td>\n      <td>-0.0167</td>\n      <td>0.60%</td>\n      <td>126.0</td>\n      <td>-0.0360</td>\n      <td>0.64%</td>\n    </tr>\n    <tr>\n      <td>1</td>\n      <td>2020-12-01</td>\n      <td>122.1</td>\n      <td>-0.0355</td>\n      <td>-1.53%</td>\n      <td>117.3</td>\n      <td>-0.0282</td>\n      <td>-1.92%</td>\n      <td>125.2</td>\n      <td>-0.0413</td>\n      <td>-1.42%</td>\n    </tr>\n    <tr>\n      <td>2</td>\n      <td>2020-11-01</td>\n      <td>124.0</td>\n      <td>-0.0048</td>\n      <td>1.89%</td>\n      <td>119.6</td>\n      <td>0.0136</td>\n      <td>2.57%</td>\n      <td>127.0</td>\n      <td>-0.0147</td>\n      <td>1.44%</td>\n    </tr>\n    <tr>\n      <td>3</td>\n      <td>2020-10-01</td>\n      <td>121.7</td>\n      <td>-0.0209</td>\n      <td>1.00%</td>\n      <td>116.6</td>\n      <td>-0.0102</td>\n      <td>1.39%</td>\n      <td>125.2</td>\n      <td>-0.0257</td>\n      <td>0.81%</td>\n    </tr>\n    <tr>\n      <td>4</td>\n      <td>2020-09-01</td>\n      <td>120.5</td>\n      <td>-0.0290</td>\n      <td>3.52%</td>\n      <td>115.0</td>\n      <td>-0.0271</td>\n      <td>3.79%</td>\n      <td>124.2</td>\n      <td>-0.0297</td>\n      <td>3.41%</td>\n    </tr>\n  </tbody>\n</table>\n</div>"},"execution_count":66}],"source":"CCI.head()"},{"cell_type":"code","execution_count":67,"id":"interstate-frequency","metadata":{"jupyter":{},"tags":[],"slideshow":{"slide_type":"slide"},"id":"E489215EA9B0496782F74982B9A93BD9","trusted":true,"collapsed":false,"scrolled":false},"outputs":[{"output_type":"execute_result","metadata":{},"data":{"text/plain":"       Month  FE_Reserve FE_Reserve_YOY FE_Reserve_Comparative  Gold_Reserve  \\\n0  2021年02月份    32049.94          3.16%                 -0.18%          6264   \n1  2021年01月份    32106.71          3.05%                 -0.18%          6264   \n2  2020年12月份    32165.22          3.49%                  1.20%          6264   \n3  2020年11月份    31784.90          2.68%                  1.61%          6264   \n4  2020年10月份    31279.82          0.73%                 -0.46%          6264   \n\n  Gold_Reserve_YOY Gold_Reserve_Comparative  \n0            0.00%                    0.00%  \n1            0.00%                    0.00%  \n2            0.00%                    0.00%  \n3            0.00%                    0.00%  \n4            0.00%                    0.00%  ","text/html":"<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>Month</th>\n      <th>FE_Reserve</th>\n      <th>FE_Reserve_YOY</th>\n      <th>FE_Reserve_Comparative</th>\n      <th>Gold_Reserve</th>\n      <th>Gold_Reserve_YOY</th>\n      <th>Gold_Reserve_Comparative</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <td>0</td>\n      <td>2021年02月份</td>\n      <td>32049.94</td>\n      <td>3.16%</td>\n      <td>-0.18%</td>\n      <td>6264</td>\n      <td>0.00%</td>\n      <td>0.00%</td>\n    </tr>\n    <tr>\n      <td>1</td>\n      <td>2021年01月份</td>\n      <td>32106.71</td>\n      <td>3.05%</td>\n      <td>-0.18%</td>\n      <td>6264</td>\n      <td>0.00%</td>\n      <td>0.00%</td>\n    </tr>\n    <tr>\n      <td>2</td>\n      <td>2020年12月份</td>\n      <td>32165.22</td>\n      <td>3.49%</td>\n      <td>1.20%</td>\n      <td>6264</td>\n      <td>0.00%</td>\n      <td>0.00%</td>\n    </tr>\n    <tr>\n      <td>3</td>\n      <td>2020年11月份</td>\n      <td>31784.90</td>\n      <td>2.68%</td>\n      <td>1.61%</td>\n      <td>6264</td>\n      <td>0.00%</td>\n      <td>0.00%</td>\n    </tr>\n    <tr>\n      <td>4</td>\n      <td>2020年10月份</td>\n      <td>31279.82</td>\n      <td>0.73%</td>\n      <td>-0.46%</td>\n      <td>6264</td>\n      <td>0.00%</td>\n      <td>0.00%</td>\n    </tr>\n  </tbody>\n</table>\n</div>"},"execution_count":67}],"source":"Gold_foregin.head()"},{"cell_type":"code","execution_count":68,"id":"academic-mumbai","metadata":{"jupyter":{},"tags":[],"slideshow":{"slide_type":"slide"},"id":"02551791081F4D538EEEF10DB24B5DA0","trusted":true,"collapsed":false,"scrolled":false},"outputs":[],"source":"cleardata(Gold_foregin)"},{"cell_type":"code","execution_count":69,"id":"orange-array","metadata":{"jupyter":{},"tags":[],"slideshow":{"slide_type":"slide"},"id":"56770C1865804A7C839C84FDD72BF2B9","trusted":true,"collapsed":false,"scrolled":false},"outputs":[{"output_type":"execute_result","metadata":{},"data":{"text/plain":"       Month  Current_Month Curent_Month_YOY Current_Month_Comparattive  \\\n0  2021年02月份           27.0          -86.43%                    -99.70%   \n1  2020年12月份         -376.0         -388.30%                   -104.30%   \n2  2020年11月份         -184.0         -360.00%                   -177.30%   \n3  2020年10月份          238.0            2264%                    800.00%   \n4  2020年09月份          -34.0           35.85%                   -117.60%   \n\n    Total  \n0  9149.0  \n1  8672.0  \n2  9048.0  \n3  9232.0  \n4  8994.0  ","text/html":"<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>Month</th>\n      <th>Current_Month</th>\n      <th>Curent_Month_YOY</th>\n      <th>Current_Month_Comparattive</th>\n      <th>Total</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <td>0</td>\n      <td>2021年02月份</td>\n      <td>27.0</td>\n      <td>-86.43%</td>\n      <td>-99.70%</td>\n      <td>9149.0</td>\n    </tr>\n    <tr>\n      <td>1</td>\n      <td>2020年12月份</td>\n      <td>-376.0</td>\n      <td>-388.30%</td>\n      <td>-104.30%</td>\n      <td>8672.0</td>\n    </tr>\n    <tr>\n      <td>2</td>\n      <td>2020年11月份</td>\n      <td>-184.0</td>\n      <td>-360.00%</td>\n      <td>-177.30%</td>\n      <td>9048.0</td>\n    </tr>\n    <tr>\n      <td>3</td>\n      <td>2020年10月份</td>\n      <td>238.0</td>\n      <td>2264%</td>\n      <td>800.00%</td>\n      <td>9232.0</td>\n    </tr>\n    <tr>\n      <td>4</td>\n      <td>2020年09月份</td>\n      <td>-34.0</td>\n      <td>35.85%</td>\n      <td>-117.60%</td>\n      <td>8994.0</td>\n    </tr>\n  </tbody>\n</table>\n</div>"},"execution_count":69}],"source":"Foreign_Exchange.head()"},{"cell_type":"code","execution_count":70,"id":"permanent-finance","metadata":{"jupyter":{},"tags":[],"slideshow":{"slide_type":"slide"},"id":"B67A07B1947A4696B3A38362B0CAC850","trusted":true,"collapsed":false,"scrolled":false},"outputs":[],"source":"cleardata(Foreign_Exchange)"},{"cell_type":"code","execution_count":71,"id":"assured-conditions","metadata":{"jupyter":{},"tags":[],"slideshow":{"slide_type":"slide"},"id":"7CDF34F6DB5E489895441FFF34D8B0C6","trusted":true,"collapsed":false,"scrolled":false},"outputs":[],"source":"New_Credit.head()\nNew_Credit.columns=[\"Month\",\"NC_Current_Month\",\"NC_Current_Month_YOY\",\"NC_Current_Month_Comparattive\",\"NC_Total\",\"NC_Total_YOY\"]"},{"cell_type":"code","execution_count":72,"id":"conventional-communist","metadata":{"jupyter":{},"tags":[],"slideshow":{"slide_type":"slide"},"id":"2FDD236FF011401688136AD0A8C473FA","trusted":true,"collapsed":false,"scrolled":false},"outputs":[],"source":"cleardata(New_Credit)"},{"cell_type":"code","execution_count":73,"id":"recreational-excuse","metadata":{"jupyter":{},"tags":[],"slideshow":{"slide_type":"slide"},"id":"2A7A88EDE92749048D1827A71E29095D","trusted":true,"collapsed":false,"scrolled":false},"outputs":[{"output_type":"execute_result","metadata":{},"data":{"text/plain":"        Date   Ratte  Change_BP   Type\n0   2021/2/7  1.6013     -33.66  99231\n1   2021/2/2  2.3435     -52.10  99231\n2  2021/2/24  1.5832     -43.30  99231\n3  2021/2/20  1.4985     -41.11  99231\n4  2021/2/10  2.0096      17.10  99231","text/html":"<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>Date</th>\n      <th>Ratte</th>\n      <th>Change_BP</th>\n      <th>Type</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <td>0</td>\n      <td>2021/2/7</td>\n      <td>1.6013</td>\n      <td>-33.66</td>\n      <td>99231</td>\n    </tr>\n    <tr>\n      <td>1</td>\n      <td>2021/2/2</td>\n      <td>2.3435</td>\n      <td>-52.10</td>\n      <td>99231</td>\n    </tr>\n    <tr>\n      <td>2</td>\n      <td>2021/2/24</td>\n      <td>1.5832</td>\n      <td>-43.30</td>\n      <td>99231</td>\n    </tr>\n    <tr>\n      <td>3</td>\n      <td>2021/2/20</td>\n      <td>1.4985</td>\n      <td>-41.11</td>\n      <td>99231</td>\n    </tr>\n    <tr>\n      <td>4</td>\n      <td>2021/2/10</td>\n      <td>2.0096</td>\n      <td>17.10</td>\n      <td>99231</td>\n    </tr>\n  </tbody>\n</table>\n</div>"},"execution_count":73}],"source":"CHIBOR.head()"},{"cell_type":"code","execution_count":74,"id":"excessive-washington","metadata":{"jupyter":{},"tags":[],"slideshow":{"slide_type":"slide"},"id":"CCBABCB2C2DD4A99AE1B09DCEF355996","trusted":true,"collapsed":false,"scrolled":false},"outputs":[{"output_type":"stream","text":"<class 'pandas.core.frame.DataFrame'>\nRangeIndex: 25880 entries, 0 to 25879\nData columns (total 4 columns):\nDate         25880 non-null object\nRatte        25880 non-null float64\nChange_BP    25880 non-null float64\nType         25880 non-null int64\ndtypes: float64(2), int64(1), object(1)\nmemory usage: 808.9+ KB\n","name":"stdout"}],"source":"CHIBOR.info()"},{"cell_type":"code","execution_count":75,"id":"exempt-found","metadata":{"jupyter":{},"tags":[],"slideshow":{"slide_type":"slide"},"id":"0B73DE3B032446878133380B5B9C5FD7","trusted":true,"collapsed":false,"scrolled":false},"outputs":[{"output_type":"execute_result","metadata":{},"data":{"text/plain":"  Announced_Date Effective_Date Benchmark_Deposit_Rate_BeforeAdj  \\\n0     2015/10/23     2015/10/24                            1.75%   \n1      2015/8/25      2015/8/26                            2.00%   \n2      2015/6/27      2015/6/28                            2.25%   \n3      2015/5/10      2015/5/11                            2.50%   \n4      2015/2/28       2015/3/1                            2.75%   \n\n  Benchmark_Deposit_Rate_AfterAdj Benchmark_Deposit_Rate_Ajd  \\\n0                           1.50%                     -0.25%   \n1                           1.75%                     -0.25%   \n2                           2.00%                     -0.25%   \n3                           2.25%                     -0.25%   \n4                           2.50%                     -0.25%   \n\n  Benchmark_Loan_Rate_BeforeAdj Benchmark_Loan_Rate_AfterAdj  \\\n0                         4.60%                        4.35%   \n1                         4.85%                        4.60%   \n2                         5.10%                        4.85%   \n3                         5.35%                        5.10%   \n4                         5.60%                        5.35%   \n\n  Benchmark_Loan_Rate_Ajd Next_Day_SHIndex Next_Day_SZIndex  Unnamed: 10  \n0                  -0.25%            0.50%            0.73%          NaN  \n1                  -0.25%           -1.27%           -2.92%          NaN  \n2                  -0.25%           -3.34%           -5.78%          NaN  \n3                  -0.25%            3.04%            3.20%          NaN  \n4                  -0.25%            0.78%            1.07%          NaN  ","text/html":"<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>Announced_Date</th>\n      <th>Effective_Date</th>\n      <th>Benchmark_Deposit_Rate_BeforeAdj</th>\n      <th>Benchmark_Deposit_Rate_AfterAdj</th>\n      <th>Benchmark_Deposit_Rate_Ajd</th>\n      <th>Benchmark_Loan_Rate_BeforeAdj</th>\n      <th>Benchmark_Loan_Rate_AfterAdj</th>\n      <th>Benchmark_Loan_Rate_Ajd</th>\n      <th>Next_Day_SHIndex</th>\n      <th>Next_Day_SZIndex</th>\n      <th>Unnamed: 10</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <td>0</td>\n      <td>2015/10/23</td>\n      <td>2015/10/24</td>\n      <td>1.75%</td>\n      <td>1.50%</td>\n      <td>-0.25%</td>\n      <td>4.60%</td>\n      <td>4.35%</td>\n      <td>-0.25%</td>\n      <td>0.50%</td>\n      <td>0.73%</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <td>1</td>\n      <td>2015/8/25</td>\n      <td>2015/8/26</td>\n      <td>2.00%</td>\n      <td>1.75%</td>\n      <td>-0.25%</td>\n      <td>4.85%</td>\n      <td>4.60%</td>\n      <td>-0.25%</td>\n      <td>-1.27%</td>\n      <td>-2.92%</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <td>2</td>\n      <td>2015/6/27</td>\n      <td>2015/6/28</td>\n      <td>2.25%</td>\n      <td>2.00%</td>\n      <td>-0.25%</td>\n      <td>5.10%</td>\n      <td>4.85%</td>\n      <td>-0.25%</td>\n      <td>-3.34%</td>\n      <td>-5.78%</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <td>3</td>\n      <td>2015/5/10</td>\n      <td>2015/5/11</td>\n      <td>2.50%</td>\n      <td>2.25%</td>\n      <td>-0.25%</td>\n      <td>5.35%</td>\n      <td>5.10%</td>\n      <td>-0.25%</td>\n      <td>3.04%</td>\n      <td>3.20%</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <td>4</td>\n      <td>2015/2/28</td>\n      <td>2015/3/1</td>\n      <td>2.75%</td>\n      <td>2.50%</td>\n      <td>-0.25%</td>\n      <td>5.60%</td>\n      <td>5.35%</td>\n      <td>-0.25%</td>\n      <td>0.78%</td>\n      <td>1.07%</td>\n      <td>NaN</td>\n    </tr>\n  </tbody>\n</table>\n</div>"},"execution_count":75}],"source":"Rate_Adjustment.head()"},{"cell_type":"code","execution_count":76,"id":"chinese-dominant","metadata":{"jupyter":{},"tags":[],"slideshow":{"slide_type":"slide"},"id":"314080E74FD7401E8E51AA979F8C14AD","trusted":true,"collapsed":false,"scrolled":false},"outputs":[{"output_type":"execute_result","metadata":{},"data":{"text/plain":"  Adjust_Date  Gas_Price Gas_Price_Change  Diesel_Price Diesel_Price_Change\n0   2021/3/18       7675             ↑235          6690                ↑230\n1    2021/3/4       7440             ↑260          6460                ↑250\n2   2021/2/19       7180             ↑275          6210                ↑265\n3    2021/2/1       6905              ↑75          5945                 ↑70\n4   2021/1/18       6830             ↑185          5875                ↑180","text/html":"<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>Adjust_Date</th>\n      <th>Gas_Price</th>\n      <th>Gas_Price_Change</th>\n      <th>Diesel_Price</th>\n      <th>Diesel_Price_Change</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <td>0</td>\n      <td>2021/3/18</td>\n      <td>7675</td>\n      <td>↑235</td>\n      <td>6690</td>\n      <td>↑230</td>\n    </tr>\n    <tr>\n      <td>1</td>\n      <td>2021/3/4</td>\n      <td>7440</td>\n      <td>↑260</td>\n      <td>6460</td>\n      <td>↑250</td>\n    </tr>\n    <tr>\n      <td>2</td>\n      <td>2021/2/19</td>\n      <td>7180</td>\n      <td>↑275</td>\n      <td>6210</td>\n      <td>↑265</td>\n    </tr>\n    <tr>\n      <td>3</td>\n      <td>2021/2/1</td>\n      <td>6905</td>\n      <td>↑75</td>\n      <td>5945</td>\n      <td>↑70</td>\n    </tr>\n    <tr>\n      <td>4</td>\n      <td>2021/1/18</td>\n      <td>6830</td>\n      <td>↑185</td>\n      <td>5875</td>\n      <td>↑180</td>\n    </tr>\n  </tbody>\n</table>\n</div>"},"execution_count":76}],"source":"Gas_price.head()"},{"cell_type":"code","execution_count":77,"id":"union-trinidad","metadata":{"jupyter":{},"tags":[],"slideshow":{"slide_type":"slide"},"id":"28D0357B38834DC38BADEC6B219A5459","trusted":true,"collapsed":false,"scrolled":false},"outputs":[{"output_type":"stream","text":"<class 'pandas.core.frame.DataFrame'>\nRangeIndex: 214 entries, 0 to 213\nData columns (total 5 columns):\nAdjust_Date            214 non-null object\nGas_Price              214 non-null int64\nGas_Price_Change       214 non-null object\nDiesel_Price           214 non-null int64\nDiesel_Price_Change    214 non-null object\ndtypes: int64(2), object(3)\nmemory usage: 8.5+ KB\n","name":"stdout"}],"source":"Gas_price.info()"},{"cell_type":"code","execution_count":78,"id":"structured-dancing","metadata":{"jupyter":{},"tags":[],"slideshow":{"slide_type":"slide"},"id":"A95FB67146874AAA8C6B56587B8836A3","trusted":true,"collapsed":false,"scrolled":false},"outputs":[{"output_type":"execute_result","metadata":{},"data":{"text/plain":"       Date Shanghai_Issued_TotalCapital Shenzhen_Issued_TotalCapital  \\\n0   2021年3月                            -                            -   \n1   2021年2月                        42851                        23034   \n2   2021年1月                        42674                        22978   \n3   2021年累计                          NaN                          NaN   \n4  2020年12月                        42601                        22855   \n\n  Shanghai_MarketValue Shenzhen_MarketValue Shanghai_Trading_Value  \\\n0                    -                    -                      -   \n1               461069               339722                  65364   \n2               457015               344614                  96861   \n3                  NaN                  NaN                 162225   \n4               455322               341917                  84058   \n\n  Shenzhen_Trading_Value Shanghai_Trading_Volume Shenzhen_Trading_Volume  \\\n0                      -                       -                       -   \n1                  79355                    4856                    5319   \n2                 121853                    6654                    7981   \n3                 201208                   11510                   13300   \n4                 104352                    6482                    7855   \n\n   Shanghai_Index_A_Max  Shenzhen_Index_A_Max  Shanghai_Index_A_Min  \\\n0                3578.0                2473.0                3328.0   \n1                3732.0                2629.0                3466.0   \n2                3637.0                2602.0                3447.0   \n3                   NaN                   NaN                   NaN   \n4                3475.0                2438.0                3325.0   \n\n   Shenzhen_Index_A_Min  \n0                2279.0  \n1                2428.0  \n2                2485.0  \n3                   NaN  \n4                2353.0  ","text/html":"<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>Date</th>\n      <th>Shanghai_Issued_TotalCapital</th>\n      <th>Shenzhen_Issued_TotalCapital</th>\n      <th>Shanghai_MarketValue</th>\n      <th>Shenzhen_MarketValue</th>\n      <th>Shanghai_Trading_Value</th>\n      <th>Shenzhen_Trading_Value</th>\n      <th>Shanghai_Trading_Volume</th>\n      <th>Shenzhen_Trading_Volume</th>\n      <th>Shanghai_Index_A_Max</th>\n      <th>Shenzhen_Index_A_Max</th>\n      <th>Shanghai_Index_A_Min</th>\n      <th>Shenzhen_Index_A_Min</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <td>0</td>\n      <td>2021年3月</td>\n      <td>-</td>\n      <td>-</td>\n      <td>-</td>\n      <td>-</td>\n      <td>-</td>\n      <td>-</td>\n      <td>-</td>\n      <td>-</td>\n      <td>3578.0</td>\n      <td>2473.0</td>\n      <td>3328.0</td>\n      <td>2279.0</td>\n    </tr>\n    <tr>\n      <td>1</td>\n      <td>2021年2月</td>\n      <td>42851</td>\n      <td>23034</td>\n      <td>461069</td>\n      <td>339722</td>\n      <td>65364</td>\n      <td>79355</td>\n      <td>4856</td>\n      <td>5319</td>\n      <td>3732.0</td>\n      <td>2629.0</td>\n      <td>3466.0</td>\n      <td>2428.0</td>\n    </tr>\n    <tr>\n      <td>2</td>\n      <td>2021年1月</td>\n      <td>42674</td>\n      <td>22978</td>\n      <td>457015</td>\n      <td>344614</td>\n      <td>96861</td>\n      <td>121853</td>\n      <td>6654</td>\n      <td>7981</td>\n      <td>3637.0</td>\n      <td>2602.0</td>\n      <td>3447.0</td>\n      <td>2485.0</td>\n    </tr>\n    <tr>\n      <td>3</td>\n      <td>2021年累计</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>162225</td>\n      <td>201208</td>\n      <td>11510</td>\n      <td>13300</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <td>4</td>\n      <td>2020年12月</td>\n      <td>42601</td>\n      <td>22855</td>\n      <td>455322</td>\n      <td>341917</td>\n      <td>84058</td>\n      <td>104352</td>\n      <td>6482</td>\n      <td>7855</td>\n      <td>3475.0</td>\n      <td>2438.0</td>\n      <td>3325.0</td>\n      <td>2353.0</td>\n    </tr>\n  </tbody>\n</table>\n</div>"},"execution_count":78}],"source":"National_Sock_Trading.head()"},{"cell_type":"code","execution_count":79,"id":"excessive-scenario","metadata":{"jupyter":{},"tags":[],"slideshow":{"slide_type":"slide"},"id":"ECA445A8E12C4BE6887B83FF8160E5D2","trusted":true,"collapsed":false,"scrolled":false},"outputs":[],"source":"cleardata(National_Sock_Trading)"},{"cell_type":"code","execution_count":80,"id":"british-bouquet","metadata":{"jupyter":{},"tags":[],"slideshow":{"slide_type":"slide"},"id":"4F4BC4D965BF402388FBC0FF97B29287","trusted":true,"collapsed":false,"scrolled":false},"outputs":[{"output_type":"execute_result","metadata":{},"data":{"text/plain":"         Date Shanghai_Issued_TotalCapital Shenzhen_Issued_TotalCapital  \\\n1     2021年2月                        42851                        23034   \n2     2021年1月                        42674                        22978   \n3     2021年累计                          NaN                          NaN   \n4    2020年12月                        42601                        22855   \n5    2020年11月                        42306                        22704   \n..        ...                          ...                          ...   \n168   2008年4月                        14786                         3006   \n169   2008年3月                        14476                         2903   \n170   2008年2月                        14301                         2853   \n171   2008年1月                        14198                         2819   \n172   2008年累计                          NaN                          NaN   \n\n    Shanghai_MarketValue Shenzhen_MarketValue Shanghai_Trading_Value  \\\n1                 461069               339722                  65364   \n2                 457015               344614                  96861   \n3                    NaN                  NaN                 162225   \n4                 455322               341917                  84058   \n5                 441857               327666                  72843   \n..                   ...                  ...                    ...   \n168               194727                45517                  18649   \n169               181350                45439                  19419   \n170               225846                55787                  14230   \n171               225355                52500                  30760   \n172                  NaN                  NaN                 180430   \n\n    Shenzhen_Trading_Value Shanghai_Trading_Volume Shenzhen_Trading_Volume  \\\n1                    79355                    4856                    5319   \n2                   121853                    6654                    7981   \n3                   201208                   11510                   13300   \n4                   104352                    6482                    7855   \n5                   102926                    5698                    7381   \n..                     ...                     ...                     ...   \n168                   8253                    1369                   600.6   \n169                   9114                    1291                     575   \n170                   6881                   861.3                   396.8   \n171                  15771                    1772                   874.1   \n172                  86683                   16312                    7820   \n\n     Shanghai_Index_A_Max  Shenzhen_Index_A_Max  Shanghai_Index_A_Min  \\\n1                  3732.0                2629.0                3466.0   \n2                  3637.0                2602.0                3447.0   \n3                     NaN                   NaN                   NaN   \n4                  3475.0                2438.0                3325.0   \n5                  3457.0                2450.0                3210.0   \n..                    ...                   ...                   ...   \n168                3705.0                1160.0                2991.0   \n169                4472.0                1499.0                3357.0   \n170                4696.0                1527.0                4123.0   \n171                5523.0                1668.0                4331.0   \n172                   NaN                   NaN                   NaN   \n\n     Shenzhen_Index_A_Min  \n1                  2428.0  \n2                  2485.0  \n3                     NaN  \n4                  2353.0  \n5                  2332.0  \n..                    ...  \n168                 963.8  \n169                1187.0  \n170                1385.0  \n171                1424.0  \n172                   NaN  \n\n[172 rows x 13 columns]","text/html":"<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>Date</th>\n      <th>Shanghai_Issued_TotalCapital</th>\n      <th>Shenzhen_Issued_TotalCapital</th>\n      <th>Shanghai_MarketValue</th>\n      <th>Shenzhen_MarketValue</th>\n      <th>Shanghai_Trading_Value</th>\n      <th>Shenzhen_Trading_Value</th>\n      <th>Shanghai_Trading_Volume</th>\n      <th>Shenzhen_Trading_Volume</th>\n      <th>Shanghai_Index_A_Max</th>\n      <th>Shenzhen_Index_A_Max</th>\n      <th>Shanghai_Index_A_Min</th>\n      <th>Shenzhen_Index_A_Min</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <td>1</td>\n      <td>2021年2月</td>\n      <td>42851</td>\n      <td>23034</td>\n      <td>461069</td>\n      <td>339722</td>\n      <td>65364</td>\n      <td>79355</td>\n      <td>4856</td>\n      <td>5319</td>\n      <td>3732.0</td>\n      <td>2629.0</td>\n      <td>3466.0</td>\n      <td>2428.0</td>\n    </tr>\n    <tr>\n      <td>2</td>\n      <td>2021年1月</td>\n      <td>42674</td>\n      <td>22978</td>\n      <td>457015</td>\n      <td>344614</td>\n      <td>96861</td>\n      <td>121853</td>\n      <td>6654</td>\n      <td>7981</td>\n      <td>3637.0</td>\n      <td>2602.0</td>\n      <td>3447.0</td>\n      <td>2485.0</td>\n    </tr>\n    <tr>\n      <td>3</td>\n      <td>2021年累计</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>162225</td>\n      <td>201208</td>\n      <td>11510</td>\n      <td>13300</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <td>4</td>\n      <td>2020年12月</td>\n      <td>42601</td>\n      <td>22855</td>\n      <td>455322</td>\n      <td>341917</td>\n      <td>84058</td>\n      <td>104352</td>\n      <td>6482</td>\n      <td>7855</td>\n      <td>3475.0</td>\n      <td>2438.0</td>\n      <td>3325.0</td>\n      <td>2353.0</td>\n    </tr>\n    <tr>\n      <td>5</td>\n      <td>2020年11月</td>\n      <td>42306</td>\n      <td>22704</td>\n      <td>441857</td>\n      <td>327666</td>\n      <td>72843</td>\n      <td>102926</td>\n      <td>5698</td>\n      <td>7381</td>\n      <td>3457.0</td>\n      <td>2450.0</td>\n      <td>3210.0</td>\n      <td>2332.0</td>\n    </tr>\n    <tr>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n    </tr>\n    <tr>\n      <td>168</td>\n      <td>2008年4月</td>\n      <td>14786</td>\n      <td>3006</td>\n      <td>194727</td>\n      <td>45517</td>\n      <td>18649</td>\n      <td>8253</td>\n      <td>1369</td>\n      <td>600.6</td>\n      <td>3705.0</td>\n      <td>1160.0</td>\n      <td>2991.0</td>\n      <td>963.8</td>\n    </tr>\n    <tr>\n      <td>169</td>\n      <td>2008年3月</td>\n      <td>14476</td>\n      <td>2903</td>\n      <td>181350</td>\n      <td>45439</td>\n      <td>19419</td>\n      <td>9114</td>\n      <td>1291</td>\n      <td>575</td>\n      <td>4472.0</td>\n      <td>1499.0</td>\n      <td>3357.0</td>\n      <td>1187.0</td>\n    </tr>\n    <tr>\n      <td>170</td>\n      <td>2008年2月</td>\n      <td>14301</td>\n      <td>2853</td>\n      <td>225846</td>\n      <td>55787</td>\n      <td>14230</td>\n      <td>6881</td>\n      <td>861.3</td>\n      <td>396.8</td>\n      <td>4696.0</td>\n      <td>1527.0</td>\n      <td>4123.0</td>\n      <td>1385.0</td>\n    </tr>\n    <tr>\n      <td>171</td>\n      <td>2008年1月</td>\n      <td>14198</td>\n      <td>2819</td>\n      <td>225355</td>\n      <td>52500</td>\n      <td>30760</td>\n      <td>15771</td>\n      <td>1772</td>\n      <td>874.1</td>\n      <td>5523.0</td>\n      <td>1668.0</td>\n      <td>4331.0</td>\n      <td>1424.0</td>\n    </tr>\n    <tr>\n      <td>172</td>\n      <td>2008年累计</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>180430</td>\n      <td>86683</td>\n      <td>16312</td>\n      <td>7820</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n    </tr>\n  </tbody>\n</table>\n<p>172 rows × 13 columns</p>\n</div>"},"execution_count":80}],"source":"National_Sock_Trading.drop(index=0)"},{"cell_type":"code","execution_count":81,"id":"upset-rubber","metadata":{"jupyter":{},"tags":[],"slideshow":{"slide_type":"slide"},"id":"0A678A8525EF46C189AA8CC493C38847","trusted":true,"collapsed":false,"scrolled":false},"outputs":[{"output_type":"execute_result","metadata":{},"data":{"text/plain":"       Date Shanghai_Issued_TotalCapital Shenzhen_Issued_TotalCapital  \\\n0   2021年3月                            -                            -   \n1   2021年2月                        42851                        23034   \n2   2021年1月                        42674                        22978   \n3   2021年累计                          NaN                          NaN   \n4  2020年12月                        42601                        22855   \n\n  Shanghai_MarketValue Shenzhen_MarketValue Shanghai_Trading_Value  \\\n0                    -                    -                      -   \n1               461069               339722                  65364   \n2               457015               344614                  96861   \n3                  NaN                  NaN                 162225   \n4               455322               341917                  84058   \n\n  Shenzhen_Trading_Value Shanghai_Trading_Volume Shenzhen_Trading_Volume  \\\n0                      -                       -                       -   \n1                  79355                    4856                    5319   \n2                 121853                    6654                    7981   \n3                 201208                   11510                   13300   \n4                 104352                    6482                    7855   \n\n   Shanghai_Index_A_Max  Shenzhen_Index_A_Max  Shanghai_Index_A_Min  \\\n0                3578.0                2473.0                3328.0   \n1                3732.0                2629.0                3466.0   \n2                3637.0                2602.0                3447.0   \n3                   NaN                   NaN                   NaN   \n4                3475.0                2438.0                3325.0   \n\n   Shenzhen_Index_A_Min  \n0                2279.0  \n1                2428.0  \n2                2485.0  \n3                   NaN  \n4                2353.0  ","text/html":"<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>Date</th>\n      <th>Shanghai_Issued_TotalCapital</th>\n      <th>Shenzhen_Issued_TotalCapital</th>\n      <th>Shanghai_MarketValue</th>\n      <th>Shenzhen_MarketValue</th>\n      <th>Shanghai_Trading_Value</th>\n      <th>Shenzhen_Trading_Value</th>\n      <th>Shanghai_Trading_Volume</th>\n      <th>Shenzhen_Trading_Volume</th>\n      <th>Shanghai_Index_A_Max</th>\n      <th>Shenzhen_Index_A_Max</th>\n      <th>Shanghai_Index_A_Min</th>\n      <th>Shenzhen_Index_A_Min</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <td>0</td>\n      <td>2021年3月</td>\n      <td>-</td>\n      <td>-</td>\n      <td>-</td>\n      <td>-</td>\n      <td>-</td>\n      <td>-</td>\n      <td>-</td>\n      <td>-</td>\n      <td>3578.0</td>\n      <td>2473.0</td>\n      <td>3328.0</td>\n      <td>2279.0</td>\n    </tr>\n    <tr>\n      <td>1</td>\n      <td>2021年2月</td>\n      <td>42851</td>\n      <td>23034</td>\n      <td>461069</td>\n      <td>339722</td>\n      <td>65364</td>\n      <td>79355</td>\n      <td>4856</td>\n      <td>5319</td>\n      <td>3732.0</td>\n      <td>2629.0</td>\n      <td>3466.0</td>\n      <td>2428.0</td>\n    </tr>\n    <tr>\n      <td>2</td>\n      <td>2021年1月</td>\n      <td>42674</td>\n      <td>22978</td>\n      <td>457015</td>\n      <td>344614</td>\n      <td>96861</td>\n      <td>121853</td>\n      <td>6654</td>\n      <td>7981</td>\n      <td>3637.0</td>\n      <td>2602.0</td>\n      <td>3447.0</td>\n      <td>2485.0</td>\n    </tr>\n    <tr>\n      <td>3</td>\n      <td>2021年累计</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>162225</td>\n      <td>201208</td>\n      <td>11510</td>\n      <td>13300</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <td>4</td>\n      <td>2020年12月</td>\n      <td>42601</td>\n      <td>22855</td>\n      <td>455322</td>\n      <td>341917</td>\n      <td>84058</td>\n      <td>104352</td>\n      <td>6482</td>\n      <td>7855</td>\n      <td>3475.0</td>\n      <td>2438.0</td>\n      <td>3325.0</td>\n      <td>2353.0</td>\n    </tr>\n  </tbody>\n</table>\n</div>"},"execution_count":81}],"source":"National_Sock_Trading.head()"},{"cell_type":"code","execution_count":82,"id":"designing-january","metadata":{"jupyter":{},"tags":[],"slideshow":{"slide_type":"slide"},"id":"FBB24811DFBC4E5880065BED9EA2B202","trusted":true,"collapsed":false,"scrolled":false},"outputs":[{"output_type":"execute_result","metadata":{},"data":{"text/plain":"  Data_Date  New_Investor_Acct New_Investor_Comparative New_Investor_YOY  \\\n0   2021年2月              160.9                  -23.15%           79.74%   \n1   2021年1月              209.4                   29.13%          161.60%   \n2  2020年12月              162.2                    6.20%          100.40%   \n3  2020年11月              152.7                   36.34%           84.77%   \n4  2020年10月              112.0                  -27.33%           41.11%   \n\n   Ending_Investors  Ending_Investors_A  Ending_Investors_B Hushen_Total  \\\n0          18147.87            18086.26              239.71      80.08万亿   \n1          17986.92            17925.22              239.70      80.16万亿   \n2          17777.49            17715.72              239.71      79.72万亿   \n3          17615.31            17553.45              239.72      76.95万亿   \n4          17462.60            17400.30              239.78      73.58万亿   \n\n  Hushen_Avg  Shanghai_Securities_Composite_Index  Shanghai_index_Growth_Rate  \n0     44.28万                              3509.08                        0.75  \n1     44.72万                              3483.07                        0.29  \n2     45.00万                              3473.07                        2.40  \n3     43.84万                              3391.76                        5.19  \n4     42.29万                              3224.53                        0.20  ","text/html":"<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>Data_Date</th>\n      <th>New_Investor_Acct</th>\n      <th>New_Investor_Comparative</th>\n      <th>New_Investor_YOY</th>\n      <th>Ending_Investors</th>\n      <th>Ending_Investors_A</th>\n      <th>Ending_Investors_B</th>\n      <th>Hushen_Total</th>\n      <th>Hushen_Avg</th>\n      <th>Shanghai_Securities_Composite_Index</th>\n      <th>Shanghai_index_Growth_Rate</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <td>0</td>\n      <td>2021年2月</td>\n      <td>160.9</td>\n      <td>-23.15%</td>\n      <td>79.74%</td>\n      <td>18147.87</td>\n      <td>18086.26</td>\n      <td>239.71</td>\n      <td>80.08万亿</td>\n      <td>44.28万</td>\n      <td>3509.08</td>\n      <td>0.75</td>\n    </tr>\n    <tr>\n      <td>1</td>\n      <td>2021年1月</td>\n      <td>209.4</td>\n      <td>29.13%</td>\n      <td>161.60%</td>\n      <td>17986.92</td>\n      <td>17925.22</td>\n      <td>239.70</td>\n      <td>80.16万亿</td>\n      <td>44.72万</td>\n      <td>3483.07</td>\n      <td>0.29</td>\n    </tr>\n    <tr>\n      <td>2</td>\n      <td>2020年12月</td>\n      <td>162.2</td>\n      <td>6.20%</td>\n      <td>100.40%</td>\n      <td>17777.49</td>\n      <td>17715.72</td>\n      <td>239.71</td>\n      <td>79.72万亿</td>\n      <td>45.00万</td>\n      <td>3473.07</td>\n      <td>2.40</td>\n    </tr>\n    <tr>\n      <td>3</td>\n      <td>2020年11月</td>\n      <td>152.7</td>\n      <td>36.34%</td>\n      <td>84.77%</td>\n      <td>17615.31</td>\n      <td>17553.45</td>\n      <td>239.72</td>\n      <td>76.95万亿</td>\n      <td>43.84万</td>\n      <td>3391.76</td>\n      <td>5.19</td>\n    </tr>\n    <tr>\n      <td>4</td>\n      <td>2020年10月</td>\n      <td>112.0</td>\n      <td>-27.33%</td>\n      <td>41.11%</td>\n      <td>17462.60</td>\n      <td>17400.30</td>\n      <td>239.78</td>\n      <td>73.58万亿</td>\n      <td>42.29万</td>\n      <td>3224.53</td>\n      <td>0.20</td>\n    </tr>\n  </tbody>\n</table>\n</div>"},"execution_count":82}],"source":"Stock_Accountt_Status.head()"},{"cell_type":"code","execution_count":83,"id":"productive-milwaukee","metadata":{"jupyter":{},"tags":[],"slideshow":{"slide_type":"slide"},"id":"746BADF2D895494FB3E9D6FE3FDEC144","trusted":true,"collapsed":false,"scrolled":false},"outputs":[],"source":"# cleardata(Stock_Accountt_Status)"},{"cell_type":"code","execution_count":84,"id":"hydraulic-nursing","metadata":{"jupyter":{},"tags":[],"slideshow":{"slide_type":"slide"},"id":"46149672144C421985FBBC4783AC4592","trusted":true,"collapsed":false,"scrolled":false},"outputs":[],"source":"Stock_Accountt_Status[\"Month\"] = pd.to_datetime(Stock_Accountt_Status[\"Data_Date\"],format=\"%Y年%m月\")"},{"cell_type":"code","execution_count":85,"id":"heard-grade","metadata":{"scrolled":false,"jupyter":{},"tags":[],"slideshow":{"slide_type":"slide"},"id":"EA12CB8B17F14CF08D2DC0A539DD2BB1","trusted":true,"collapsed":false},"outputs":[{"output_type":"execute_result","metadata":{},"data":{"text/plain":"         Month    YOY  Cumulative_Growth\n0   2021-02-01  0.000               35.1\n1   2020-12-01  0.073                2.8\n2   2020-11-01  0.070                2.3\n3   2020-10-01  0.069                1.8\n4   2020-09-01  0.069                1.2\n..         ...    ...                ...\n139 2008-06-01  0.160               16.3\n140 2008-05-01  0.160               16.3\n141 2008-04-01  0.157               16.3\n142 2008-03-01  0.178               16.4\n143 2008-02-01  0.154               15.4\n\n[144 rows x 3 columns]","text/html":"<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>Month</th>\n      <th>YOY</th>\n      <th>Cumulative_Growth</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <td>0</td>\n      <td>2021-02-01</td>\n      <td>0.000</td>\n      <td>35.1</td>\n    </tr>\n    <tr>\n      <td>1</td>\n      <td>2020-12-01</td>\n      <td>0.073</td>\n      <td>2.8</td>\n    </tr>\n    <tr>\n      <td>2</td>\n      <td>2020-11-01</td>\n      <td>0.070</td>\n      <td>2.3</td>\n    </tr>\n    <tr>\n      <td>3</td>\n      <td>2020-10-01</td>\n      <td>0.069</td>\n      <td>1.8</td>\n    </tr>\n    <tr>\n      <td>4</td>\n      <td>2020-09-01</td>\n      <td>0.069</td>\n      <td>1.2</td>\n    </tr>\n    <tr>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n    </tr>\n    <tr>\n      <td>139</td>\n      <td>2008-06-01</td>\n      <td>0.160</td>\n      <td>16.3</td>\n    </tr>\n    <tr>\n      <td>140</td>\n      <td>2008-05-01</td>\n      <td>0.160</td>\n      <td>16.3</td>\n    </tr>\n    <tr>\n      <td>141</td>\n      <td>2008-04-01</td>\n      <td>0.157</td>\n      <td>16.3</td>\n    </tr>\n    <tr>\n      <td>142</td>\n      <td>2008-03-01</td>\n      <td>0.178</td>\n      <td>16.4</td>\n    </tr>\n    <tr>\n      <td>143</td>\n      <td>2008-02-01</td>\n      <td>0.154</td>\n      <td>15.4</td>\n    </tr>\n  </tbody>\n</table>\n<p>144 rows × 3 columns</p>\n</div>"},"execution_count":85}],"source":"Industrial_add"},{"cell_type":"code","execution_count":86,"id":"directed-blues","metadata":{"jupyter":{},"tags":[],"slideshow":{"slide_type":"slide"},"id":"A6A0E70F1F90407A803FB9EA94EF7785","trusted":true,"collapsed":false,"scrolled":false},"outputs":[],"source":"data_list=[PMI,FDI,CPI,RMB_Foreg,Fiscal_Re,Urban_Fixed,Housing_price,Industrial_add,Import_export,Money_supply,CPGI,RSCG,CCI,Gold_foregin,Foreign_Exchange,New_Credit,Stock_Accountt_Status]"},{"cell_type":"markdown","id":"loose-freeze","metadata":{"scrolled":false,"jupyter":{},"tags":[],"slideshow":{"slide_type":"slide"},"id":"0088FB11BCC04B3C85FC2A237F32B722","trusted":true,"collapsed":false,"mdEditEnable":false},"source":"### 数据集成\n   我们定义列表里的DataFrame，使用循环手段，集成数据\n"},{"metadata":{"id":"DDDE32A4B6764DDA9C593EBDACC2464B","notebookId":"60b33cef4223f3001719a7b1","jupyter":{},"tags":[],"slideshow":{"slide_type":"slide"},"trusted":true},"cell_type":"code","outputs":[],"source":"dataset=PPI[[\"Month\"]]\n# data=PPI.copy()\nfor data in data_list:\n#     print(data)\n    print(type(data))\n#     print(len(dataset)) \n#     print(data)\n    dataset=dataset.merge(data,how=\"left\",on=\"Month\")\n#     print(len(dataset))\n#     print(i)","execution_count":null},{"cell_type":"code","execution_count":89,"id":"fatal-graduate","metadata":{"jupyter":{},"tags":[],"slideshow":{"slide_type":"slide"},"id":"EDF4A3FF454F4B958E8E23D71085F37F","trusted":true,"collapsed":false,"scrolled":false},"outputs":[],"source":"# 保存数据为eco_part\ndataset.to_csv(\"../input/eco_part.csv\")"},{"cell_type":"code","execution_count":90,"id":"relative-leisure","metadata":{"jupyter":{},"tags":[],"slideshow":{"slide_type":"slide"},"id":"4908853A0D904A8B8CB0E64EBCD56FF9","trusted":true,"collapsed":false,"scrolled":false},"outputs":[{"output_type":"stream","text":"<class 'pandas.core.frame.DataFrame'>\nInt64Index: 182 entries, 0 to 181\nColumns: 114 entries, Month to Shanghai_index_Growth_Rate\ndtypes: datetime64[ns](1), float64(87), object(26)\nmemory usage: 163.5+ KB\n","name":"stdout"}],"source":"dataset.info()"},{"cell_type":"code","execution_count":91,"id":"expired-african","metadata":{"jupyter":{},"tags":[],"slideshow":{"slide_type":"slide"},"id":"DEF8A7BE30AF4BAE80B41B8432E23B7C","trusted":true,"collapsed":false,"scrolled":false},"outputs":[{"output_type":"execute_result","metadata":{},"data":{"text/plain":"       Month  Manufacturing_Industry_Index  Manufacturing_YOY  \\\n0 2021-02-01                          50.6             0.4174   \n1 2021-01-01                          51.3             0.0260   \n2 2020-12-01                          51.9             0.0339   \n3 2020-11-01                          52.1             0.0378   \n4 2020-10-01                          51.4             0.0426   \n\n   Nonmanufacturing_Industry_Index  Nonmanufacturing_YOY  Current_Month_FDI  \\\n0                             51.4                0.7365                NaN   \n1                             52.4               -0.0314                NaN   \n2                             55.7                0.0411                NaN   \n3                             56.4                0.0368                NaN   \n4                             56.2                0.0644              118.3   \n\n   FDI_YOY  FDI_Comparative_Rate FDI_Total  FDI_Total_YOY  ...  \\\n0      NaN                   NaN       NaN            NaN  ...   \n1      NaN                   NaN       NaN            NaN  ...   \n2      NaN                   NaN       NaN            NaN  ...   \n3      NaN                   NaN       NaN            NaN  ...   \n4   0.1832               -0.1704       NaN            NaN  ...   \n\n   New_Investor_Acct  New_Investor_Comparative  New_Investor_YOY  \\\n0              160.9                   -23.15%            79.74%   \n1              209.4                    29.13%           161.60%   \n2              162.2                     6.20%           100.40%   \n3              152.7                    36.34%            84.77%   \n4              112.0                   -27.33%            41.11%   \n\n   Ending_Investors  Ending_Investors_A  Ending_Investors_B  Hushen_Total  \\\n0          18147.87            18086.26              239.71       80.08万亿   \n1          17986.92            17925.22              239.70       80.16万亿   \n2          17777.49            17715.72              239.71       79.72万亿   \n3          17615.31            17553.45              239.72       76.95万亿   \n4          17462.60            17400.30              239.78       73.58万亿   \n\n   Hushen_Avg  Shanghai_Securities_Composite_Index  Shanghai_index_Growth_Rate  \n0      44.28万                              3509.08                        0.75  \n1      44.72万                              3483.07                        0.29  \n2      45.00万                              3473.07                        2.40  \n3      43.84万                              3391.76                        5.19  \n4      42.29万                              3224.53                        0.20  \n\n[5 rows x 114 columns]","text/html":"<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>Month</th>\n      <th>Manufacturing_Industry_Index</th>\n      <th>Manufacturing_YOY</th>\n      <th>Nonmanufacturing_Industry_Index</th>\n      <th>Nonmanufacturing_YOY</th>\n      <th>Current_Month_FDI</th>\n      <th>FDI_YOY</th>\n      <th>FDI_Comparative_Rate</th>\n      <th>FDI_Total</th>\n      <th>FDI_Total_YOY</th>\n      <th>...</th>\n      <th>New_Investor_Acct</th>\n      <th>New_Investor_Comparative</th>\n      <th>New_Investor_YOY</th>\n      <th>Ending_Investors</th>\n      <th>Ending_Investors_A</th>\n      <th>Ending_Investors_B</th>\n      <th>Hushen_Total</th>\n      <th>Hushen_Avg</th>\n      <th>Shanghai_Securities_Composite_Index</th>\n      <th>Shanghai_index_Growth_Rate</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <td>0</td>\n      <td>2021-02-01</td>\n      <td>50.6</td>\n      <td>0.4174</td>\n      <td>51.4</td>\n      <td>0.7365</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>...</td>\n      <td>160.9</td>\n      <td>-23.15%</td>\n      <td>79.74%</td>\n      <td>18147.87</td>\n      <td>18086.26</td>\n      <td>239.71</td>\n      <td>80.08万亿</td>\n      <td>44.28万</td>\n      <td>3509.08</td>\n      <td>0.75</td>\n    </tr>\n    <tr>\n      <td>1</td>\n      <td>2021-01-01</td>\n      <td>51.3</td>\n      <td>0.0260</td>\n      <td>52.4</td>\n      <td>-0.0314</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>...</td>\n      <td>209.4</td>\n      <td>29.13%</td>\n      <td>161.60%</td>\n      <td>17986.92</td>\n      <td>17925.22</td>\n      <td>239.70</td>\n      <td>80.16万亿</td>\n      <td>44.72万</td>\n      <td>3483.07</td>\n      <td>0.29</td>\n    </tr>\n    <tr>\n      <td>2</td>\n      <td>2020-12-01</td>\n      <td>51.9</td>\n      <td>0.0339</td>\n      <td>55.7</td>\n      <td>0.0411</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>...</td>\n      <td>162.2</td>\n      <td>6.20%</td>\n      <td>100.40%</td>\n      <td>17777.49</td>\n      <td>17715.72</td>\n      <td>239.71</td>\n      <td>79.72万亿</td>\n      <td>45.00万</td>\n      <td>3473.07</td>\n      <td>2.40</td>\n    </tr>\n    <tr>\n      <td>3</td>\n      <td>2020-11-01</td>\n      <td>52.1</td>\n      <td>0.0378</td>\n      <td>56.4</td>\n      <td>0.0368</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>...</td>\n      <td>152.7</td>\n      <td>36.34%</td>\n      <td>84.77%</td>\n      <td>17615.31</td>\n      <td>17553.45</td>\n      <td>239.72</td>\n      <td>76.95万亿</td>\n      <td>43.84万</td>\n      <td>3391.76</td>\n      <td>5.19</td>\n    </tr>\n    <tr>\n      <td>4</td>\n      <td>2020-10-01</td>\n      <td>51.4</td>\n      <td>0.0426</td>\n      <td>56.2</td>\n      <td>0.0644</td>\n      <td>118.3</td>\n      <td>0.1832</td>\n      <td>-0.1704</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>...</td>\n      <td>112.0</td>\n      <td>-27.33%</td>\n      <td>41.11%</td>\n      <td>17462.60</td>\n      <td>17400.30</td>\n      <td>239.78</td>\n      <td>73.58万亿</td>\n      <td>42.29万</td>\n      <td>3224.53</td>\n      <td>0.20</td>\n    </tr>\n  </tbody>\n</table>\n<p>5 rows × 114 columns</p>\n</div>"},"execution_count":91}],"source":"dataset.head()"},{"metadata":{"id":"E59CE3188D774000860E989E83843D7D","notebookId":"60b33cef4223f3001719a7b1","jupyter":{},"tags":[],"slideshow":{"slide_type":"slide"},"trusted":true},"cell_type":"code","outputs":[],"source":"","execution_count":null}],"metadata":{"kernelspec":{"language":"python","display_name":"Python 3","name":"python3"},"language_info":{"codemirror_mode":{"name":"ipython","version":3},"name":"python","mimetype":"text/x-python","nbconvert_exporter":"python","file_extension":".py","version":"3.5.2","pygments_lexer":"ipython3"}},"nbformat":4,"nbformat_minor":5}